Phrase placement for optimizing digital page

ABSTRACT

Techniques for improving the accuracy, relevancy, and efficiency of a computer system of an online service by providing a user interface to optimize a digital page of a user on the online service are disclosed herein. In some embodiments, a computer system receives a plurality of phrases, and then, for each one of the plurality of phrases, selects a corresponding section of a page of a first user to suggest for placement of the phrase from amongst a plurality of sections using a placement classifier, and generates a corresponding recommendation for the page of a first user based on the phrase and the determined corresponding section of the page of the first user, with the recommendation comprising a suggested addition of the phrase to the determined corresponding section of the page of the first user.

TECHNICAL FIELD

The present application relates generally to systems, methods, andcomputer program products for improving the accuracy, relevancy, andefficiency of a computer system of an online service by providing a userinterface to optimize a digital page of a user on the online service.

BACKGROUND

Digital pages of users of online services often omit relevant data. Thislack of data can cause technical problems in the performance of theonline service. For example, in situations where the online service isperforming a search based on search criteria for a certain type of data,pages are often omitted from the search because their profiles lack thattype of data even though they would have satisfied the search criteriaif the page had included the corresponding data. As a result, theaccuracy, relevancy, and completeness of the search results arediminished. Additionally, since otherwise relevant search results areomitted, users often spend a longer time on their search, consumingelectronic resources (e.g., network bandwidth, computational expense ofserver performing search). Thus, the function of the computer system ofthe online service suffers. Furthermore, the prior art lacks aconvenient and efficient way for users to add such relevant data totheir pages or to specific sections of their pages. Other technicalproblems may arise as well.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments of the present disclosure are illustrated by way ofexample and not limitation in the figures of the accompanying drawings,in which like reference numbers indicate similar elements.

FIG. 1 is a block diagram illustrating a client-server system, inaccordance with an example embodiment.

FIG. 2 is a block diagram showing the functional components of a socialnetworking service within a networked system, in accordance with anexample embodiment.

FIG. 3 is a block diagram illustrating an optimization system, inaccordance with an example embodiment.

FIG. 4 illustrates a graphical user interface (GUI) in which a profilepage of a user is displayed, in accordance with an example embodiment.

FIG. 5 illustrates a GUI in which a job posting published on an onlineservice is displayed, in accordance with an example embodiment.

FIG. 6 illustrates a GUI in which a user can submit an application for ajob posting, in accordance with an example embodiment.

FIG. 7 illustrates a GUI in which recommendations for optimizing a pageof a user are displayed, in accordance with an example embodiment.

FIG. 8 illustrates a GUI in which a user can save user-entered text to asection of a page of the user, in accordance with an example embodiment.

FIG. 9 is a flowchart illustrating a method of providing arecommendations for optimizing a page of a user, in accordance with anexample embodiment.

FIG. 10 is a flowchart illustrating a method of displaying a page of auser, in accordance with an example embodiment.

FIG. 11 is a flowchart illustrating another method of providing arecommendations for optimizing a page of a user, in accordance with anexample embodiment.

FIG. 12 is a flowchart illustrating yet another method of providing arecommendations for optimizing a page of a user, in accordance with anexample embodiment.

FIG. 13 is a flowchart illustrating yet another method of providing arecommendations for optimizing a page of a user, in accordance with anexample embodiment.

FIG. 14 is a flowchart illustrating a method of providing a suggestionfor optimizing a page of a user, in accordance with an exampleembodiment.

FIG. 15 is a flowchart illustrating a method of training a classifier tobe used in providing a suggestion for optimizing a page of a user, inaccordance with an example embodiment.

FIG. 16 is a block diagram illustrating a mobile device, in accordancewith some example embodiments.

FIG. 17 is a block diagram of an example computer system on whichmethodologies described herein may be executed, in accordance with anexample embodiment.

DETAILED DESCRIPTION I. Overview

Example methods and systems of improving the accuracy, relevancy, andefficiency of a computer system of an online service by providing a userinterface to optimize a digital page of a user on the online service aredisclosed. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of example embodiments. It will be evident, however, toone skilled in the art that the present embodiments may be practicedwithout these specific details.

Some or all of the above problems may be addressed by one or moreexample embodiments disclosed herein, which provide methods and userinterfaces for adding accurate and relevant data to a page of a user onan online service in a convenient and efficient manner. In some exampleembodiments, a computer system identifies job postings corresponding toa type of job that a user is interested in or is likely to be interestedin based on feature data (e.g., a role within an organization, aseniority level, an industry) of the job postings, and then extractsphrases from the identified job postings, giving preference to phrasesthat are most relevant to the type of job of the job postings, whileenforcing sufficient diversity amongst the extracted phrases in order toavoid redundancy and wasted display space. For each one of the extractedphrases, the computer system determines a corresponding section of apage of the user to suggest for placement of the extracted phrase usinga placement classifier, and then generates a correspondingrecommendation for the page of the user based on the extracted phraseand the corresponding section of the extracted phrase. Eachrecommendation comprises a suggested addition of the correspondingphrase to the corresponding section of the page of the user. Thegenerated recommendations are displayed on a computing device of theuser. In some example embodiments, selectable user interface elementscorresponding to the generated recommendations are displayed andconfigured to enable the user to conveniently and efficiently add thephrases, or portions thereof, to the page of the user.

Each of the steps of identifying job postings, extracting phrases fromthe identified job postings, determining corresponding sections of apage to suggest for placement of the extracted phrases, generatingrecommendations for the page, and displaying the generatedrecommendations involves a non-generic, unconventional, and non-routinecombination of operations. By applying one or more of the solutionsdisclosed herein, some technical effects of the system and method of thepresent disclosure are to provide a convenient and efficient way for auser of an online service to add accurate and relevant data to a page ofthe user on the online service. As a result, the functioning of thecomputer system of the online service is improved. Other technicaleffects will be apparent from this disclosure as well.

II. Detailed Example Embodiments

The methods or embodiments disclosed herein may be implemented as acomputer system having one or more modules (e.g., hardware modules orsoftware modules). Such modules may be executed by one or moreprocessors of the computer system. The methods or embodiments disclosedherein may be embodied as instructions stored on a machine-readablemedium that, when executed by one or more processors, cause the one ormore processors to perform the instructions.

FIG. 1 is a block diagram illustrating a client-server system 100, inaccordance with an example embodiment. A networked system 102 providesserver-side functionality via a network 104 (e.g., the Internet or WideArea Network (WAN)) to one or more clients. FIG. 1 illustrates, forexample, a web client 106 (e.g., a browser) and a programmatic client108 executing on respective client machines 110 and 112.

An Application Program Interface (API) server 114 and a web server 116are coupled to, and provide programmatic and web interfaces respectivelyto, one or more application servers 118. The application servers 118host one or more applications 120. The application servers 118 are, inturn, shown to be coupled to one or more database servers 124 thatfacilitate access to one or more databases 126. While the applications120 are shown in FIG. 1 to form part of the networked system 102, itwill be appreciated that, in alternative embodiments, the applications120 may form part of a service that is separate and distinct from thenetworked system 102.

Further, while the system 100 shown in FIG. 1 employs a client-serverarchitecture, the present disclosure is of course not limited to such anarchitecture, and could equally well find application in a distributed,or peer-to-peer, architecture system, for example. The variousapplications 120 could also be implemented as standalone softwareprograms, which do not necessarily have networking capabilities.

The web client 106 accesses the various applications 120 via the webinterface supported by the web server 116. Similarly, the programmaticclient 108 accesses the various services and functions provided by theapplications 120 via the programmatic interface provided by the APIserver 114.

FIG. 1 also illustrates a third party application 128, executing on athird party server machine 130, as having programmatic access to thenetworked system 102 via the programmatic interface provided by the APIserver 114. For example, the third party application 128 may, utilizinginformation retrieved from the networked system 102, support one or morefeatures or functions on a website hosted by the third party. The thirdparty website may, for example, provide one or more functions that aresupported by the relevant applications of the networked system 102.

In some embodiments, any website referred to herein may comprise onlinecontent that may be rendered on a variety of devices, including but notlimited to, a desktop personal computer, a laptop, and a mobile device(e.g., a tablet computer, smartphone, etc.). In this respect, any ofthese devices may be employed by a user to use the features of thepresent disclosure. In some embodiments, a user can use a mobile app ona mobile device (any of machines 110, 112, and 130 may be a mobiledevice) to access and browse online content, such as any of the onlinecontent disclosed herein. A mobile server (e.g., API server 114) maycommunicate with the mobile app and the application server(s) 118 inorder to make the features of the present disclosure available on themobile device.

In some embodiments, the networked system 102 may comprise functionalcomponents of a social networking service. FIG. 2 is a block diagramshowing the functional components of a social networking system 210,including a data processing module referred to herein as an optimizationsystem 216, for use in social networking system 210, consistent withsome embodiments of the present disclosure. In some embodiments, theoptimization system 216 resides on application server(s) 118 in FIG. 1.However, it is contemplated that other configurations are also withinthe scope of the present disclosure.

As shown in FIG. 2, a front end may comprise a user interface module(e.g., a web server) 212, which receives requests from variousclient-computing devices, and communicates appropriate responses to therequesting client devices. For example, the user interface module(s) 212may receive requests in the form of Hypertext Transfer Protocol (HTTP)requests, or other web-based, application programming interface (API)requests. In addition, a member interaction detection module 213 may beprovided to detect various interactions that members have with differentapplications, services and content presented. As shown in FIG. 2, upondetecting a particular interaction, the member interaction detectionmodule 213 logs the interaction, including the type of interaction andany meta-data relating to the interaction, in a member activity andbehavior database 222.

An application logic layer may include one or more various applicationserver modules 214, which, in conjunction with the user interfacemodule(s) 212, generate various user interfaces (e.g., web pages) withdata retrieved from various data sources in the data layer. With someembodiments, individual application server modules 214 are used toimplement the functionality associated with various applications and/orservices provided by the social networking service. In some exampleembodiments, the application logic layer includes the optimizationsystem 216.

As shown in FIG. 2, a data layer may include several databases, such asa database 218 for storing profile data, including both member profiledata and profile data for various organizations (e.g., companies,schools, etc.). Consistent with some embodiments, when a personinitially registers to become a member of the social networking service,the person will be prompted to provide some personal information, suchas his or her name, age (e.g., birthdate), gender, interests, contactinformation, home town, address, the names of the member's spouse and/orfamily members, educational background (e.g., schools, majors,matriculation and/or graduation dates, etc.), employment history,skills, professional organizations, and so on. This information isstored, for example, in the database 218. Similarly, when arepresentative of an organization initially registers the organizationwith the social networking service, the representative may be promptedto provide certain information about the organization. This informationmay be stored, for example, in the database 218, or another database(not shown). In some example embodiments, the profile data may beprocessed (e.g., in the background or offline) to generate variousderived profile data. For example, if a member has provided informationabout various job titles the member has held with the same company ordifferent companies, and for how long, this information can be used toinfer or derive a member profile attribute indicating the member'soverall seniority level, or seniority level within a particular company.In some example embodiments, importing or otherwise accessing data fromone or more externally hosted data sources may enhance profile data forboth members and organizations. For instance, with companies inparticular, financial data may be imported from one or more externaldata sources, and made part of a company's profile.

Once registered, a member may invite other members, or be invited byother members, to connect via the social networking service. A“connection” may require or indicate a bi-lateral agreement by themembers, such that both members acknowledge the establishment of theconnection. Similarly, with some embodiments, a member may elect to“follow” another member. In contrast to establishing a connection, theconcept of “following” another member typically is a unilateraloperation, and at least with some embodiments, does not requireacknowledgement or approval by the member that is being followed. Whenone member follows another, the member who is following may receivestatus updates (e.g., in an activity or content stream) or othermessages published by the member being followed, or relating to variousactivities undertaken by the member being followed. Similarly, when amember follows an organization, the member becomes eligible to receivemessages or status updates published on behalf of the organization. Forinstance, messages or status updates published on behalf of anorganization that a member is following will appear in the member'spersonalized data feed, commonly referred to as an activity stream orcontent stream. In any case, the various associations and relationshipsthat the members establish with other members, or with other entitiesand objects, are stored and maintained within a social graph, shown inFIG. 2 with database 220.

As members interact with the various applications, services, and contentmade available via the social networking system 210, the members'interactions and behavior (e.g., content viewed, links or buttonsselected, messages responded to, etc.) may be tracked and informationconcerning the member's activities and behavior may be logged or stored,for example, as indicated in FIG. 2 by the database 222. This loggedactivity information may then be used by the optimization system 216.The members' interactions and behavior may also be tracked, stored, andused by an optimization system 216 residing on a client device, such aswithin a browser of the client device, as will be discussed in furtherdetail below.

In some embodiments, databases 218, 220, and 222 may be incorporatedinto database(s) 126 in FIG. 1. However, other configurations are alsowithin the scope of the present disclosure.

Although not shown, in some embodiments, the social networking system210 provides an application programming interface (API) module via whichapplications and services can access various data and services providedor maintained by the social networking service. For example, using anAPI, an application may be able to request and/or receive one or morenavigation recommendations. Such applications may be browser-basedapplications, or may be operating system-specific. In particular, someapplications may reside and execute (at least partially) on one or moremobile devices (e.g., phone, or tablet computing devices) with a mobileoperating system. Furthermore, while in many cases the applications orservices that leverage the API may be applications and services that aredeveloped and maintained by the entity operating the social networkingservice, other than data privacy concerns, nothing prevents the API frombeing provided to the public or to certain third-parties under specialarrangements, thereby making the navigation recommendations available tothird party applications and services.

Although the optimization system 216 is referred to herein as being usedin the context of a social networking service, it is contemplated thatit may also be employed in the context of any website or onlineservices. Additionally, although features of the present disclosure canbe used or presented in the context of a web page, it is contemplatedthat any user interface view (e.g., a user interface on a mobile deviceor on desktop software) is within the scope of the present disclosure.

FIG. 3 is a block diagram illustrating the optimization system 216, inaccordance with an example embodiment. In some embodiments, theoptimization system 216 comprises any combination of one or more of anidentification module 310, an extraction module 320, a placement module330, a suggestion module 340, a machine learning module 350, and one ormore databases 360. The modules 310, 320, 330, 340, and 350 and thedatabase(s) 360 can reside on a computer system, or other machine,having a memory and at least one processor (not shown). In someembodiments, the modules 310, 320, 330, 340, and 350 and the database(s)360 can be incorporated into the application server(s) 118 in FIG. 1. Insome example embodiments, the database(s) 360 is incorporated intodatabase(s) 126 in FIG. 1 and can include any combination of one or moreof databases 218, 220, and 222 in FIG. 2. However, it is contemplatedthat other configurations of the modules 310, 320, 330, 340, and 350, aswell as the database(s) 360, are also within the scope of the presentdisclosure.

In some example embodiments, one or more of the modules 310, 320, 330,340, and 350 is configured to perform various communication functions tofacilitate the functionality described herein, such as by communicatingwith the social networking system 210 via the network 104 using a wiredor wireless connection. Any combination of one or more of the modules310, 320, 330, 340, and 350 may also provide various web services orfunctions, such as retrieving information from the third party servers130 and the social networking system 210. Information retrieved by theany of the modules 310, 320, 330, 340, and 350 may include profile datacorresponding to users and members of the social networking service ofthe social networking system 210.

Additionally, any combination of one or more of the modules 310, 320,330, 340, and 350 can provide various data functionality, such asexchanging information with the database(s) 360 or servers. For example,any of the modules 310, 320, 330, 340, and 350 can access memberprofiles that include profile data from the database(s) 360, as well asextract attributes and/or characteristics from the profile data ofmember profiles. Furthermore, the one or more of the modules 310, 320,330, 340, and 350 can access profile data, social graph data, and memberactivity and behavior data from the database(s) 360, as well as exchangeinformation with third party servers 130, client machines 110, 112, andother sources of information.

In some example embodiments, the optimization system 216 is configuredto provide a convenient and efficient way for users to add relevant datato their pages or to specific sections of their pages, providing userswith insights and suggestions about what they should change on theirpages, such as their profile pages and resumes, to improve the qualityof their pages and to align the content of their pages with specificobjectives (e.g., career aspirations).

The optimization system 216 provides actionable suggestions designed toimprove a user's chances in pursuing his or her objectives or interests.These actionable suggestions comprise a finite set of transformationsthat can be applied to a user's page, such as a profile page of the useror a resume of the user. These transformations can be accomplished in areasonable amount of time. Examples include, but are not limited to, theaddition of particular content, improving composition, and the additionof quantitative detail.

In some example embodiments, the suggestions are based on jobs thatusers are interested in, as well as known recruiter behavior. Forexample, the optimization system 216 may suggest that a user includecertain information that recruiters look for, such as achievements andother measurable results. The optimization system 216 may also helpusers align their profiles with the jobs that they are interested in byshowing users the keywords and phrases from the descriptions of thosejobs.

In some example embodiments, the high-level objective of theoptimization system 216 is:argmax_(ƒ(r)) P(y|ƒ(r),J),where each r represents a user's current page (e.g., profile or resume),J represents a set of user job interests, ƒ(r)∈F is a transformationoutputting a new page r′, and y is a signal representing whether or nota user is a good fit for a job, j∈J. The feedback signal, y, can beestimated and measured through different data sources, as will beexplained later. The high-level objective disclosed above is extremelychallenging for the following reasons: (1) how does the optimizationsystem 216 define a user's job interests J; and (2) how does theoptimization system 216 constrain the space of pageedits/transformations F. Details of how the optimization system 216addresses these technical challenges will be discussed below.

In some example embodiments, the optimization system 216 identifies jobpostings corresponding to a type of job that a user is interested in oris likely to be interested in based on feature data (e.g., a role withinan organization, a seniority level, an industry) of the job postings,and then extracts phrases from the identified job postings, givingpreference to phrases that are most relevant to the type of job of thejob postings, while enforcing sufficient diversity amongst the extractedphrases in order to avoid redundancy and wasted display space. For eachone of the extracted phrases, the optimization system 216 determines acorresponding section of a page of the user (e.g., a profile page or aresume) to suggest for placement of the extracted phrase using aplacement classifier, and then generates a corresponding recommendationfor the page of the user based on the extracted phrase and thecorresponding section of the extracted phrase. Each recommendationcomprises a suggested addition of the corresponding phrase to thecorresponding section of the page of the user. The generatedrecommendations are displayed on a computing device of the user. In someexample embodiments, selectable user interface elements corresponding tothe generated recommendations are displayed and configured to enable theuser to conveniently and efficiently add the phrases, or portionsthereof, to the page of the user.

FIG. 4 illustrates a graphical user interface (GUI) 400 in which aprofile page of a user is displayed, in accordance with an exampleembodiment. The profile page displayed in the GUI 400 comprises profiledata 410 of the user. In the example shown in FIG. 4, the profile data410 includes headline data 410-1 identifying the user (e.g., photo andname), the user's current position at a particular organization, and theuser's current residential location, summary data 410-2, experience data410-3, and featured skill and endorsement data 410-4. Other types ofprofile data 410 are also within the scope of the present disclosure. Insome example embodiments, the GUI 400 displays each type of profile data410 in its own dedicated section of profile page.

FIG. 5 illustrates a GUI 500 in which a job posting published on anonline service is displayed, in accordance with an example embodiment.In FIG. 5, the job posting comprises headline information 510 anddetailed information 512. The headline information 510 comprises basicinformation about the job posting, such as the job title or position(e.g., “SENIOR SOFTWARE DESIGNER”), the name of the company ororganization seeking applicants for the job title or position (e.g.,“LINKEDIN”), and the location of the job (e.g., “SAN FRANCISCO BAYAREA”). The detailed information 512 comprises more detailed informationabout the job, including, but not limited to, a job description, aseniority level of the job, one or more industries to which the jobcorresponds, an employment type for the job, and requirements for thejob. In FIG. 5, the GUI 500 also comprises a selectable user interfaceelement 520 configured to enable a user who is viewing the job postingto submit a job application for the job posting. In some exampleembodiments, the selectable user interface element 520 comprises aselectable button or link (e.g., the selectable “APPLY” button in FIG.5) that is configured to, when selected, trigger the social networkingsystem 210 to display another GUI in which the user can submit anapplication for the job posting.

FIG. 6 illustrates a GUI 600 in which a user can submit an applicationfor a job posting, in accordance with an example embodiment. In someexample embodiments, the GUI 600 comprises one or more user interfaceelements configured to enable the user to submit contact information,such as an e-mail address and/or a destination for receiving a phonecall and/or text messages (e.g., a phone number). For example, the GUI600 comprises a text field 610 configured to receive an e-mail addressof the user, as well as a text field 612 configured to receive adestination for receiving a phone call and/or text messages. In someexample embodiments, the GUI 600 also comprises one or more userinterface elements configured to enable the user to submit a resume. Forexample, the GUI 600 comprises a selectable user interface element 620configured to enable the user to upload a resume in a certain format,such as a Microsoft Word document or a Portable Document Format (PDF).In response to the user selecting the selectable user interface element620, the social networking system 210 may display a window (not shown)in which a user may select a file containing a resume to upload. Afterthe user has entered contact information and uploaded a resume, the usermay submit the entered contact information and the uploaded resume fileto the social networking system 210 for processing using a selectableuser interface element 630 (e.g., a “SUBMIT APPLICATION” button). Theentered contact information and the uploaded resume file may form a jobapplication of the user, who is now recognized by the social networkingsystem 210 as an applicant for the job posting based on the submissionof the entered contact information and the uploaded resume. The uploadedresume may be stored in the database(s) 360 in association with the userto whom the uploaded resume corresponds.

In some example embodiments, the identification module 310 is configuredto identify a plurality of job postings published on an online serviceas corresponding to a type of job based on corresponding feature data ofeach one of the plurality of job postings. In some example embodiments,the corresponding feature data of each one of the plurality of jobpostings comprises at least one of a role within an organization, aseniority level, an industry, and a job function. However, other typesof feature data are also within the scope of the present disclosure.

In some example embodiments, the identifying of the plurality of jobscomprises receiving a plurality of job postings published on an onlineservice, determining that a subset of the plurality of the job postingssatisfies a similarity criteria based on corresponding feature data ofeach job posting in the subset, with the subset comprising multiple jobpostings, and selecting the subset of the plurality of job postingsbased on the determining that the subset satisfies the similaritycriteria. In some example embodiments, the receiving the plurality ofjob postings comprises accessing user activity data of a user stored ina database in association with a profile of the user, determining thatthe user activity data indicates an interest by the user in theplurality of job postings, and selecting the plurality of job postingsbased on the determining that the user activity data indicates aninterest by the first user in the plurality of job openings. The useractivity data may comprise at least one of viewing a job listing andsubmitting an application for a job listing. However, other types ofuser activity data are also within the scope of the present disclosure.

In some example embodiments, the determining that the subset of theplurality of the job postings satisfies the similarity criteriacomprises using at least one filter to determine that the correspondingfeature data of each job posting in the subset of the plurality of jobpostings matches a filter feature data. In one example, the filterfeature data identifies “COMPUTER SOFTWARE” as the industry data and thesimilarity criteria requires that the corresponding industry data ofeach job posting in the subset of the plurality of job postings includes“COMPUTER SOFTWARE.” In some example embodiments, the determining thatthe subset of the plurality of the job postings satisfies the similaritycriteria comprises using semantic matching to determine that thecorresponding feature data of each job posting in the subset of theplurality of job postings comprises a similar meaning as thecorresponding feature data of the other job postings in the subset ofthe plurality of job postings, rather than requiring an exact match.

In some example embodiments, the extraction module 320 is configured toextract a plurality of phrases from the identified plurality of jobpostings based on a corresponding relevancy measurement and acorresponding diversity measurement for each one of the plurality ofphrases. The relevancy measurement comprises a measure of relevance ofthe corresponding phrase to the type of job, and the diversitymeasurement comprises a measure of distinction between the correspondingphrase and other phrases in the plurality of phrases.

In some example embodiments, the extracting of the plurality of phrasescomprises receiving a plurality of phrases for a type of job. Thereceiving of the plurality of phrases for the type of job may compriseselecting sentences from one or more job listings of the type of jobbased on the selected sentences being determined to compriserole-dependent information that corresponds to a role in anorganization, and extracting noun phrases from the selected sentences,with the extracted noun phrases being included in the plurality ofphrases, and a remaining portion of the selected sentences other thanthe extracted noun phrases being omitted from the plurality of phrases.In some example embodiments, the receiving of the plurality of phrasesfor the type of job comprises extracting the plurality of phrases fromone or more job listings of the type of job.

In some example embodiments, the extracting of the plurality of phrasesfurther comprises selecting a group of phrases from the plurality ofphrases based on a corresponding relevancy measurement and acorresponding diversity measurement for each phrase in the selectedgroup of phrases. The relevancy measurement comprises a measure ofrelevance of the corresponding selected phrase in the selected group ofphrases to the type of job, and the diversity measurement comprises ameasure of distinction between each phrase in the selected group ofphrases and other phrases in the selected group of the phrases. In someexample embodiments, the selecting the group of phrases from theplurality of phrases comprises generating the corresponding relevancemeasurement for each one of the plurality of phrases, ranking theplurality of phrases based on their corresponding relevancemeasurements, selecting a first phrase of the plurality of phrases forinclusion in the group of phrases based on the first phrase having ahighest ranking amongst the plurality of phrases, identifying a secondphrase of the plurality of phrases based on the second phrase having asecond highest ranking amongst the plurality of phrases, determining adiversity measurement of the second phrase indicating the measure ofdistinction between the second phrase and the first phrase, anddetermining whether or not to include the second phrase in the group ofphrases based on the determined diversity measurement of the secondphrase.

In some example embodiments, the placement module 330 is configured to,for each one of the extracted plurality of phrases, determine acorresponding section of a page of a user to suggest for placement ofthe extracted phrase using a placement classifier. The placementclassifier is configured to determine the corresponding section based onthe extracted phrase. In some example embodiments, the plurality ofsections comprises at least one of a summary section, a skill section, awork experience section, and an education section. However, other typesof sections are also within the scope of the present disclosure. In someexample embodiments, the page comprises a profile page of the user thatis associated with a profile of the user, as discussed above withrespect to FIG. 4, or a resume of the user that is included in anapplication to a job posting via the online service, as discussed abovewith respect to FIG. 6. However, other types of pages of the user arealso within the scope of the present disclosure. In some exampleembodiments, for each one of the extracted plurality of phrases, thecorresponding section of the page comprises one of a summary section ofa profile, a work experience section of the profile, an educationsection of the profile, a skills section of the profile, and anaccomplishments section of the profile. However, other types of sectionsof the page are also within the scope of the present disclosure.

In some example embodiments, the suggestion module 340 is configured to,for each one of the extracted plurality of phrases, generate acorresponding recommendation for the page of the user based on theextracted phrase and the determined corresponding section of theextracted phrase. The corresponding recommendation may comprise asuggested addition of the corresponding extracted phrase to thecorresponding section of the page of the user. However, other types ofrecommendations are also within the scope of the present disclosure.

In some example embodiments, the generating of the correspondingrecommendation comprises accessing a profile of a user of an onlineservice stored in a database of the online service, and generating asuggestion for adding a measurable accomplishment to a particularsection of a page of the user based on profile data of the accessedprofile using a neural network model. The neural network model isconfigured to identify the measurable accomplishment based on theprofile data of the accessed profile. In some example embodiments, theprofile data comprises a current job title of the user and textual datadistinct from the current job title, and the neural network model isconfigured to identify the measurable accomplishment based on thecurrent job title of the user and the textual data. The textual data maycomprise text from a summary section of the profile of the user or textfrom a work experience section of the profile of the user, and themeasurable accomplishment may comprise at least a portion of the textualdata. However, other configurations of the textual data and themeasurable accomplishment are also within the scope of the presentdisclosure. In some example embodiments, the profile data furthercomprises at least one of a seniority level of the first user, alocation of the first user, an industry of the first user, and a role ofthe first user within an organization. However, other types of profiledata are also within the scope of the present disclosure.

In some example embodiments, the suggestion module 340 is furtherconfigured to cause the generated recommendations to be displayed on acomputing device of the user. In some example embodiments, thesuggestion module 340 causes a corresponding selectable user interfaceelement to be displayed in association with each one of the generatedrecommendations. FIG. 7 illustrates a GUI 700 in which recommendations710 and 720 for optimizing a page of a user are displayed, in accordancewith an example embodiment. In FIG. 710, The recommendations 710comprise suggestions of changes to be made to the page of the user.These recommendations 710 may apply to different aspects of the page.For example, the recommendation 710-1 in FIG. 7 comprises a suggestionto improve the formatting of the summary section of the user's profilepage by using bullet points to improve readability, and therecommendation 710-2 in FIG. 7 comprises a suggestion to add certaintypes of measurable results to the summary section of the user's profilepage.

In some example embodiments, the suggestion to add measurable results tothe page of the user comprises one or more indications 712 of types orareas of measurable results add to the page of the user based on thedetermination of what type of job the user in interested in, such as thetype of role or the type of industry the user is interested in. Forexample, in FIG. 7, the recommendation 710-2 comprises indications 712-1and 712-2 that the user should add measurable results in the areas ofleadership and A/B testing, respectively, to the page of the user inorder to attract recruiters of senior software engineers. Other types ofrecommendations 710 are also within the scope of the present disclosure.Examples of other types of recommendations 710 include, but are notlimited to, a recommendation to edit the page so that the descriptionsection of the page and the title section of the page are more closelyconnected (e.g., the content of the description in consistent with andincludes text from the title).

In FIG. 7, the recommendations 720 comprise suggestions to addparticular phrases to the page of the user. For example, therecommendations 720-1, 720-2, 720-3, 72-4, and 720-5 in FIG. 7 includesuggestions to add particular phrases to the page of the user. Thesesuggestions may comprise indications of an area or topic to which thesuggestion applies (e.g., leadership, A/B testing, collaboration,engineer, user research), how important the area or topic is to a typeof job that the user is interested in (e.g., high importance, mediumimportance), and the particular suggested phrase (e.g., “Coached my teamon a business strategy”).

In the example shown in FIG. 7, the recommendations 720-1, 720-2, 720-3,720-4, and 720-5 have corresponding selectable user interface elements725-1, 725-2, 725-3, 725-4, and 725-5, respectively, displayed inassociation with the recommendations 720-1, 720-2, 720-3, 720-4, and720-5. The selectable user interface elements 725 are configured to, inresponse to their selection (e.g., being clicked on, being tapped on) bythe user, cause the phrase corresponding to the selected user interfaceelement 725 to be displayed in a text field of the determinedcorresponding section of the phrase on the computing device of the user.

FIG. 8 illustrates a GUI 800 in which the user can save user-enteredtext to a section of a page of the user, in accordance with an exampleembodiment. In FIG. 8, the user has selected the selectable userinterface element 725-1 in FIG. 7, thereby triggering, or otherwisecausing, the phrase 812 corresponding to the selected user interfaceelement 725-1 to be displayed in a text field 810 of the determinedcorresponding section of the phrase 812 on the computing device of theuser. In some example embodiments, the text field 810 is configured toreceive user-entered text, such that the user may add and remove textfrom the text field 810. The phrase 812 may comprise template language,such that one or more portions of the phrase are populated by aplaceholder in which the user is encouraged to enter text. For example,although the phrase 812 shown in FIG. 8 reads “COACHED MY TEAM ON ABUSINESS STRATEGY,” the phrase 812 may alternatively read “COACHED X ONY” with “X” and “Y” serving as placeholders, or may read “COACHED ______ON ______” with “______” serving as placeholders. The GUI 800 may alsodisplay additional phrase recommendations 820. These additional phraserecommendations 820 may correspond to a select number of recommendations720 in FIG. 7 that were not yet selected by the user. In the exampleshown in FIG. 8, the GUI 800 displays additional phrase recommendation820-1, which corresponds to unselected recommendation 720-1 in FIG. 7,and additional phrase recommendation 820-2, which corresponds tounselected recommendation 720-3 in FIG. 7.

In some example embodiments, the GUI 800 also comprises a selectableuser interface element 830 configured to, in response to its selectionby the user, trigger a saving of the user-entered text that is in thetext field 810 to the determined corresponding section of the page ofthe user. The user-entered text comprises at least a portion of thephrase 812 corresponding to the selected user interface element 725. Thesuggestion module 340 is configured to store the user-entered text,which includes at least a portion of the phrase 812, in the database(s)360 in association with the determined corresponding section of the pageof the user in response to, or otherwise based on, the instruction bythe user via the selection of the selectable user interface element 830to save the user-entered text in the text field 810 to the section ofthe page of the user. As a result of this storing of the user-enteredtext in the database(s) 360 in association with the correspondingsection of the page of the user, the social networking system 210 may,in response to receiving a request to view the page of the user fromanother computing device of another user, cause the page of the user tobe displayed on the other computing device of the other user, with thepage comprising the user-entered text including at least a portion ofthe phrase 812.

In some example embodiments, the suggestion module 340 is configured toaccess a profile of the user stored in the database(s) 350, generate asuggestion for adding a measurable accomplishment to a particularsection of the profile of the user (or some other type of recommendation710 or 720) based on profile data of the accessed profile using a neuralnetwork model, and cause the generated suggestion for adding themeasurable accomplishment to be displayed on the first computing deviceof the user. The neural network model may be configured to identify themeasurable accomplishment within the profile data of the accessedprofile.

In some example embodiments, the machine learning module 350 isconfigured to train and retrain a classifier of the neural network modelto identify measurable results of the user, such as measurable resultsindicated in the accessed profile data. One technical challenge intraining the classifier is in providing enough training data toeffectively train the classifier so that the classifier is sufficientlyaccurate in its predictions, as well as to eliminate as much confusionin the predictions of the classifier. In some example embodiments, themachine learning module 350 uses training data that includes phrases inthe form of vectors. The machine learning module 350 may train theclassifier in phases. For example, in a first phase, one-thousandexamples may be labelled and used as training data in training theclassifier. The trained classifier is then used to sample a millionexamples to see where the classifier is least confident, which can beevaluated using the likelihood values of the predictions for the sampledexamples. If the likelihood value of a sampled example is very high(e.g., above 0.90) or very low (e.g., below 0.10), then the machinelearning module 350 knows that the classifier has a high level ofconfidence in its classification of the sampled example. However, whenthe likelihood value of the sampled example is around a middle (e.g.,between 0.35 and 0.65) or the classifier generates significantlydifferent likelihood values for two phrases that are very similar exceptfor minor differences, then the machine learning module 350 knows thatthe classifier is confused. In some example embodiments, the machinelearning module 350 is configured to select the most confused examplesto get labeled in the next phase of training the classifier (e.g., theretraining of the classifier).

In some example embodiments, the machine learning module 350 isconfigured to train a classifier using a first plurality of trainingdata, with each one of the first plurality of training data comprisingprofile data of the user, textual data distinct from the profile data,and a label indicating whether or not the one of the first plurality oftraining data qualifies as a measurable accomplishment. In some exampleembodiments, the machine learning module 350 is further configured to,for each one of a first plurality of sample data, generate acorresponding likelihood value indicating a likelihood that the one ofthe first plurality of sample data corresponds to a measurableaccomplishment using the trained classifier, with each one of the firstplurality of sample data comprising profile data of a user and textualdata distinct from the profile data. In some example embodiments, themachine learning module 350 is further configured to identify a portionof the first plurality of sample data as corresponding to confusedpredictions based on the corresponding likelihood values of the portionof the first plurality of sample data and a confusion criteria, and thenretrain the trained classifier using a second plurality of trainingdata, with the second plurality of training data including the portionof the first plurality of sample data based on the identifying of theportion of the first plurality of sample data as corresponding toconfused prediction. In some example embodiments, each one of the secondplurality of training data comprises profile data of a user, textualdata distinct from the profile data, and a label indicating whether ornot the one of the second plurality of training data qualifies as ameasurable accomplishment.

In some example embodiments, the machine learning module 350 isconfigured to repeat the operations of generating correspondinglikelihood values for sample data, identifying a portion of the sampleddata as corresponding to confused predictions based on the correspondinglikelihood values, and retrain the classifier using the identifiedportion of the sampled data until the portion of sample data beingidentified by the machine learning module 350 as corresponding toconfused predictions is below a threshold value (e.g., until less than2% of the samples data is identified as corresponding to confusedpredictions).

In some example embodiments, the confusion criteria comprises thecorresponding likelihood value being below a minimum threshold value andabove a maximum threshold value. For example, the confusion criteria maycomprise the corresponding likelihood value being between below 65% andabove 35%, which would be interpreted by the machine learning module 350as the classifier being confused, as the likelihood value is not veryhigh and not very low.

In some example embodiments, the confusion criteria comprises twoconditions that address the situation in the classifier has generatedsignificantly different likelihood values for two very similar, but notidentical, phrases. The first condition is that a difference between thecorresponding likelihood value of one of the portion of the plurality ofsample data and the corresponding likelihood value of another one of theportion of the plurality of sample data is greater than a thresholddifference value. The second condition is that a difference between thetextual data of the one of the portion of the plurality of sample dataand the textual data of the other one of the portion of the plurality ofsample data is less than a threshold textual difference.

FIG. 9 is a flowchart illustrating a method 900 of providing arecommendations for optimizing a page of a user, in accordance with anexample embodiment. The method 900 can be performed by processing logicthat can comprise hardware (e.g., circuitry, dedicated logic,programmable logic, microcode, etc.), software (e.g., instructions runon a processing device), or a combination thereof. In oneimplementation, the method 900 is performed by the optimization system216 of FIG. 3, or any combination of one or more of its modules, asdescribed above.

At operation 910, the optimization system 216 identifies a plurality ofjob postings published on an online service as corresponding to a typeof job based on corresponding feature data of each one of the pluralityof job postings. In some example embodiments, the corresponding featuredata of each one of the plurality of job postings comprises at least oneof a role within an organization, a seniority level, an industry, and ajob function. Other types of feature data are also within the scope ofthe present disclosure.

At operation 920, the optimization system 216 extracts a plurality ofphrases from the identified plurality of job postings based on acorresponding relevancy measurement and a corresponding diversitymeasurement for each one of the plurality of phrases. In some exampleembodiments, the relevancy measurement comprises a measure of relevanceof the corresponding phrase to the type of job, and the diversitymeasurement comprising a measure of distinction between thecorresponding phrase and other phrases in the plurality of phrases. Insome example embodiments, for each one of the extracted plurality ofphrases, the corresponding section of the page comprises one of asummary section of a profile, a work experience section of the profile,an education section of the profile, a skills section of the profile,and an accomplishments section of the profile. Other types of sectionsof the page are also within the scope of the present disclosure.

At operation 930, the optimization system 216, for each one of theextracted plurality of phrases, determines a corresponding section of apage of a first user to suggest for placement of the extracted phraseusing a placement classifier. In some example embodiments, the placementclassifier is configured to determine the corresponding section based onthe extracted phrase.

In some example embodiments, the page comprises a profile page of thefirst user that is associated with a profile of the first user, with theprofile being stored in a database of the online service in associationwith a profile of the first user. In some example embodiments, the pagecomprises a resume of the first user that is included in an applicationto a job posting via the online service. Other types of pages are alsowithin the scope of the present disclosure.

At operation 940, the optimization system 216, for each one of theextracted plurality of phrases, generates a corresponding recommendationfor the page of the first user based on the extracted phrase and thedetermined corresponding section of the extracted phrase. In someexample embodiments, the corresponding recommendation comprises asuggested addition of the corresponding extracted phrase to thecorresponding section of the page of the first user.

At operation 950, the optimization system 216 causes the generatedrecommendations to be displayed on a first computing device of the firstuser. In some example embodiments, the causing the generatedrecommendations to be displayed on the first computing device of thefirst user comprises causing a corresponding selectable user interfaceelement to be displayed in association with each one of the generatedrecommendations.

At operation 960, the optimization system 216 receives a user selectionof the corresponding selectable user interface element of one of thedisplayed recommendations from the first computing device of the firstuser

At operation 970, the optimization system 216, in response to the userselection, causes the extracted phrase corresponding to the selecteduser interface element to be displayed in a text field of the determinedcorresponding section of the extracted phrase on the first computingdevice of the first user. In some example embodiments, the text field isconfigured to receive user-entered text.

At operation 980, the optimization system 216 receives an instructionfrom the first computing device of the first user to save theuser-entered text that is in the text field to the determinedcorresponding section of the page of the first user. In some exampleembodiments, the user-entered text comprises at least a portion of theextracted phrase corresponding to the selected user interface element.

At operation 990, the optimization system 216 stores the user-enteredtext including the at least a portion of the extracted phrase in adatabase in association with the determined corresponding section of thepage of the first user in response to, or otherwise based on, theinstruction from the user received at operation 980.

It is contemplated that any of the other features described within thepresent disclosure can be incorporated into the method 900.

FIG. 10 is a flowchart illustrating a method 1000 of displaying a pageof a user, in accordance with an example embodiment. The method 1000 canbe performed by processing logic that can comprise hardware (e.g.,circuitry, dedicated logic, programmable logic, microcode, etc.),software (e.g., instructions run on a processing device), or acombination thereof. In one implementation, the method 1000 is performedby the optimization system 216 of FIG. 3, or any combination of one ormore of its modules, as described above.

In some example embodiments, the method 1000 comprises operations 1010and 1020, which are performed subsequent to operation 990 of the method900 in FIG. 9. At operation 1010, the optimization system 216 receives arequest to view the page of the first user from a second computingdevice of a second user (e.g., a different user than the user to whomthe page corresponds). At operation 1020, the optimization system 216causes the page of the first user to be displayed on the secondcomputing device of the second user in response to, or otherwise baseon, the request received at operation 1010. In some example embodiments,the page comprises the user-entered text including the at least aportion of the extracted phrase that was stored at operation 990 of themethod 900 in FIG. 9.

It is contemplated that any of the other features described within thepresent disclosure can be incorporated into the method 1000.

FIG. 11 is a flowchart illustrating another method 1100 of providing arecommendations for optimizing a page of a user, in accordance with anexample embodiment. The method 1100 can be performed by processing logicthat can comprise hardware (e.g., circuitry, dedicated logic,programmable logic, microcode, etc.), software (e.g., instructions runon a processing device), or a combination thereof. In oneimplementation, the method 1100 is performed by the optimization system216 of FIG. 3, or any combination of one or more of its modules, asdescribed above. In some example embodiments, the method 1100 comprisesoperations 1110, 1120, and 1130, which are performed prior to operation940 of the method 900 in FIG. 9.

At operation 1110, the optimization system 216 receives a plurality ofjob postings published on an online service. In some exampleembodiments, the receiving the plurality of job postings comprisesaccessing user activity data of the first user stored in a database inassociation with a profile of the first user, determining that the useractivity data indicates an interest by the first user in the pluralityof job postings, and selecting the plurality of job postings based onthe determining that the user activity data indicates an interest by thefirst user in the plurality of job openings. In some exampleembodiments, the user activity data comprises at least one of viewing ajob listing and submitting an application for a job listing. Other typesof activity data are also within the scope of the present disclosure.

At operation 1120, the optimization system 216 determines that a subsetof the plurality of the job postings satisfies a similarity criteriabased on corresponding feature data of each job posting in the subset,the subset comprising multiple job postings. In some exampleembodiments, the determining that the subset of the plurality of the jobpostings satisfies the similarity criteria comprises using at least onefilter to determine that the corresponding feature data of each jobposting in the subset of the plurality of job postings matches a filterfeature data. In some example embodiments, the determining that thesubset of the plurality of the job postings satisfies the similaritycriteria comprises using semantic matching to determine that thecorresponding feature data of each job posting in the subset of theplurality of job postings comprises a similar meaning as thecorresponding feature data of the other job postings in the subset ofthe plurality of job postings.

At operation 1130, the optimization system 216 selects the subset of theplurality of job postings based on the determination at operation 1120that the subset satisfies the similarity criteria. The method 1100 maythen proceed to operation 940, previously discussed with respect to themethod 900 of FIG. 9, in which the optimization system 216 generates arecommendation for a page of a first user based on the selected subsetof job postings, with the recommendation comprising a suggested additionof content to the page of the first user, and then operation 950,previously discussed with respect to the method 900 of FIG. 9, in whichthe optimization system 216 causes the generated recommendation for thepage of the first user to be displayed on a computing device of thefirst user.

It is contemplated that any of the other features described within thepresent disclosure can be incorporated into the method 1100.

FIG. 12 is a flowchart illustrating yet another method 1200 of providinga recommendations for optimizing a page of a user, in accordance with anexample embodiment. The method 1200 can be performed by processing logicthat can comprise hardware (e.g., circuitry, dedicated logic,programmable logic, microcode, etc.), software (e.g., instructions runon a processing device), or a combination thereof. In oneimplementation, the method 1200 is performed by the optimization system216 of FIG. 3, or any combination of one or more of its modules, asdescribed above. In some example embodiments, the method 1200 comprisesoperations 1210 and 1220, which are performed prior to operation 940 ofthe method 900 in FIG. 9.

At operation 1210, the optimization system 216 receives a plurality ofphrases for a type of job. In some example embodiments, the receivingthe plurality of phrases for the type of job comprises selectingsentences from one or more job listings of the type of job based on theselected sentences being determined to comprise role-dependentinformation that corresponds to a role in an organization, andextracting noun phrases from the selected sentences. In some exampleembodiments, the extracted noun phrases are included in the plurality ofphrases, and a remaining portion of the selected sentences other thanthe extracted noun phrases are omitted from the plurality of phrases. Insome example embodiments, the receiving the plurality of phrases for thetype of job comprises extracting the plurality of phrases from one ormore job listings of the type of job.

At operation 1220, the optimization system 216 selects a group ofphrases from the plurality of phrases based on a corresponding relevancymeasurement and a corresponding diversity measurement for each phrase inthe selected group of phrases. In some example embodiments, therelevancy measurement comprises a measure of relevance of thecorresponding selected phrase in the selected group of phrases to thetype of job, and the diversity measurement comprises a measure ofdistinction between each phrase in the selected group of phrases andother phrases in the selected group of the phrases. In some exampleembodiments, the selecting the group of phrases from the plurality ofphrases comprises generating the corresponding relevance measurement foreach one of the plurality of phrases, ranking the plurality of phrasesbased on their corresponding relevance measurements, selecting a firstphrase of the plurality of phrases for inclusion in the group of phrasesbased on the first phrase having a highest ranking amongst the pluralityof phrases, identifying a second phrase of the plurality of phrasesbased on the second phrase having a second highest ranking amongst theplurality of phrases, determining a diversity measurement of the secondphrase indicating the measure of distinction between the second phraseand the first phrase, and determining whether or not to include thesecond phrase in the group of phrases based on the determined diversitymeasurement of the second phrase.

The method 1200 may then proceed to operation 940, previously discussedwith respect to the method 900 of FIG. 9, in which the optimizationsystem 216 generates a recommendation for a page of a first user basedon the selected subset of job postings, with the recommendationcomprising a suggested addition of content to the page of the firstuser, and then operation 950, previously discussed with respect to themethod 900 of FIG. 9, in which the optimization system 216 causes thegenerated recommendation for the page of the first user to be displayedon a computing device of the first user.

It is contemplated that any of the other features described within thepresent disclosure can be incorporated into the method 1200.

FIG. 13 is a flowchart illustrating yet another method 1300 of providinga recommendations for optimizing a page of a user, in accordance with anexample embodiment. The method 1300 can be performed by processing logicthat can comprise hardware (e.g., circuitry, dedicated logic,programmable logic, microcode, etc.), software (e.g., instructions runon a processing device), or a combination thereof. In oneimplementation, the method 1300 is performed by the optimization system216 of FIG. 3, or any combination of one or more of its modules, asdescribed above. In some example embodiments, the method 1300 comprisesoperations 1310 and 1320, which are performed prior to operation 940 ofthe method 900 in FIG. 9.

At operation 1310, the optimization system 216 receives a plurality ofphrases. At operation 1320, the optimization system 216, for each one ofthe plurality of phrases, selects a corresponding section of a page of afirst user to suggest for placement of the phrase from amongst aplurality of sections using a placement classifier. In some exampleembodiments, the placement classifier is configured to determine thecorresponding section based on the phrase. In some example embodiments,the plurality of sections comprises at least one of a summary section, askill section, a work experience section, and an education section.Other types of sections are also within the scope of the presentdisclosure.

The method 1300 may then proceed to operation 940, previously discussedwith respect to the method 900 of FIG. 9, in which the optimizationsystem 216 generates a recommendation for a page of a first user basedon the selected subset of job postings, with the recommendationcomprising a suggested addition of content to the page of the firstuser, and then operation 950, previously discussed with respect to themethod 900 of FIG. 9, in which the optimization system 216 causes thegenerated recommendation for the page of the first user to be displayedon a computing device of the first user.

It is contemplated that any of the other features described within thepresent disclosure can be incorporated into the method 1300.

FIG. 14 is a flowchart illustrating a method 1400 of providing asuggestion for optimizing a page of a user, in accordance with anexample embodiment. The method 1400 can be performed by processing logicthat can comprise hardware (e.g., circuitry, dedicated logic,programmable logic, microcode, etc.), software (e.g., instructions runon a processing device), or a combination thereof. In oneimplementation, the method 1400 is performed by the optimization system216 of FIG. 3, or any combination of one or more of its modules, asdescribed above.

At operation 1410, the optimization system 216 accesses a profile of afirst user of an online service stored in a database of the onlineservice. At operation 1420, the optimization system 216 generates asuggestion for adding a measurable accomplishment to a particularsection of a page of the first user based on profile data of theaccessed profile using a neural network model, the neural network modelbeing configured to identify the measurable accomplishment based on theprofile data of the accessed profile. At operation 1430, theoptimization system 216 causes the generated suggestion for adding themeasurable accomplishment to be displayed on a first computing device ofthe first user.

In some example embodiments, the profile data comprises a current jobtitle of the first user and textual data distinct from the current jobtitle, and the neural network model is configured to identify themeasurable accomplishment based on the current job title of the firstuser and the textual data. In some example embodiments, the textual datacomprises text from a summary section of the profile of the first useror text from a work experience section of the profile of the first user,and the measurable accomplishment comprises at least a portion of thetextual data. In some example embodiments, the profile data furthercomprises at least one of a seniority level of the first user, alocation of the first user, an industry of the first user, and a role ofthe first user within an organization.

In some example embodiments, operation 1430 comprises causing aselectable user interface element to be displayed in association withthe generated suggestion. In some example embodiments, the optimizationsystem 216 receives a user selection of the selectable user interfaceelement of one of the displayed suggestion from the first computingdevice of the first user, and causes the measurable accomplishment to bedisplayed in a text field of the particular section of the page of thefirst user on the first computing device of the first user in responseto the user selection. In some example embodiments, the optimizationsystem 216 is further configured to receive an instruction from thefirst computing device of the first user to save the user-entered textthat is in the text field to the particular section of the page of thefirst user, with the user-entered text comprising at least a portion ofthe measurable accomplishment, and to store the user-entered textincluding the at least a portion of the measurable accomplishment in adatabase in association with the particular section of the page of thefirst user. In some example embodiments, the particular section of thepage comprises a summary section of the page or a work experiencesection of the page. Other types of sections of the page are also withinthe scope of the present disclosure.

It is contemplated that any of the other features described within thepresent disclosure can be incorporated into the method 1400.

FIG. 15 is a flowchart illustrating a method 1500 of training aclassifier to be used in providing a suggestion for optimizing a page ofa user, in accordance with an example embodiment. The method 1500 can beperformed by processing logic that can comprise hardware (e.g.,circuitry, dedicated logic, programmable logic, microcode, etc.),software (e.g., instructions run on a processing device), or acombination thereof. In one implementation, the method 1500 is performedby the optimization system 216 of FIG. 3, or any combination of one ormore of its modules, as described above. In some example embodiments,the method 1500 comprises operations 1510, 1520, 1530, and 1540, whichare performed prior to operation 14100 of the method 1410 in FIG. 14.

At operation 1510, the optimization system 216 trains a classifier usinga first plurality of training data. In some example embodiments, eachone of the first plurality of training data comprises profile data of auser, textual data distinct from the profile data, and a labelindicating whether or not the one of the first plurality of trainingdata qualifies as a measurable accomplishment.

At operation 1520, the optimization system 216, for each one of a firstplurality of sample data, generates a corresponding likelihood valueindicating a likelihood that the one of the first plurality of sampledata corresponds to a measurable accomplishment using the trainedclassifier. In some example embodiments, each one of the first pluralityof sample data comprises profile data of a user and textual datadistinct from the profile data.

At operation 1530, the optimization system 216 identifies a portion ofthe first plurality of sample data as corresponding to confusedpredictions based on the corresponding likelihood values of the portionof the first plurality of sample data and a confusion criteria. In someexample embodiments, the confusion criteria comprises the correspondinglikelihood value being below a minimum threshold value and above amaximum threshold value. In some example embodiments, the confusioncriteria comprises a first condition and a second condition. The firstcondition comprises a difference between the corresponding likelihoodvalue of one of the portion of the plurality of sample data and thecorresponding likelihood value of another one of the portion of theplurality of sample data being greater than a threshold differencevalue, and the second condition comprising a difference between thetextual data of the one of the portion of the plurality of sample dataand the textual data of the other one of the portion of the plurality ofsample data being less than a threshold textual difference.

At operation 1540, the optimization system 216 retrains the trainedclassifier using a second plurality of training data. In some exampleembodiments, the second plurality of training data includes the portionof the first plurality of sample data based on the identifying of theportion of the first plurality of sample data as corresponding toconfused prediction. In some example embodiments, each one of the secondplurality of training data comprises profile data of a user, textualdata distinct from the profile data, and a label indicating whether ornot the one of the second plurality of training data qualifies as ameasurable accomplishment.

The method 1500 may then proceed to operation 1410, previously discussedwith respect to the method 1400 of FIG. 14, in which the optimizationsystem 216 accesses a profile of a first user of an online servicestored in a database of the online service, and then operation 1420,previously discussed with respect to the method 1400 of FIG. 14, inwhich the optimization system 216 generates a suggestion for adding anidentified measurable accomplishment to a particular section of a pageof the first user. In some example embodiments, the optimization system216 identifies the measurable accomplishment of the first user based onprofile data of the accessed profile of the first user using theretrained classifier.

In some example embodiments, the optimization system 216 is configuredto repeat the operations 1520, 1530, and 1540, generating correspondinglikelihood values for sample data, identifying a portion of the sampleddata as corresponding to confused predictions based on the correspondinglikelihood values, and retraining the classifier using the identifiedportion of the sampled data, until the portion of sample data beingidentified by the optimization system 216 as corresponding to confusedpredictions is below a threshold value (e.g., until less than 2% of thesamples data is identified as corresponding to confused predictions).

It is contemplated that any of the other features described within thepresent disclosure can be incorporated into the method 1500.

In some example embodiments, the operations of identifying job postings,extracting phrases from the identified job postings, determiningcorresponding sections of a page to suggest for placement of theextracted phrases, generating recommendations for the page, anddisplaying the generated recommendations discussed above employ anycombination of one or more of the implementations features discussedbelow.

In some example embodiments, the optimization system 216 uses certaintechniques to evaluate free-text content. In some example embodiments,every word in free-text can be represented as a vector. Given a sequenceof training words, the objective of a word vector model employed by theoptimization system 216 may be to maximize the average log probabilityof a word given its surrounding context, such as:

$\frac{1}{T}{\sum\limits_{t = k}^{T - k}{\log\;{{p( { w_{t} \middle| w_{t - k} ,\ldots\mspace{14mu},w_{t + k}} )}.}}}$

The prediction task may be performed via a multiclass classifier, suchas a softmax or normalized exponential function:

${p( { w_{t} \middle| w_{t - k} ,\ldots\mspace{14mu},w_{t + k}} )} = {\frac{e^{y_{w_{t}}}}{\sum_{i}e^{y_{i}}}.}$Each of y_(i) is an un-normalized log-probability for each output wordi, computed as:y=b+Uh(w _(t−k) , . . . ,w _(t+k) ;W),where U,b are the softmax parameters, h is constructed by aconcatenation or average of word vectors extracted from W.

In some example embodiments, the optimization system 216 extends theword representation concept to sentences and paragraphs, such as thosein profiles and job postings. The following embedding methods haveproven effective and may be used by the optimization system 216 inrepresenting arbitrary text lengths, which may be referred to asdocuments, in order to align with common academic terminology:Doc2Vec—uses the embedding network to infer a vector for the wholedocument; FastText—infers a document embedding from the pre-trainedmodel, by averaging the pre-computed representations of the text'scomponents (words and n-grams), in a single linear pass through thetext; and Universal Sentence Encoder—uses deep averaging network tocombine multiple word representation to a sentence/paragraph or documentrepresentation.

In some example embodiments, once the algorithm to embed a set of tokensinto a vector is chosen, the optimization system 216 computes a documentembedding and computed the embedding of each candidate phraseseparately, again with the same algorithm, which may be used as an inputto a downstream ranking model. In some example embodiments, computing adocument embedding includes a noise reduction procedure, which mayinclude using only a subset of sentences in the document that are deemed“important” or using only the adjectives and nouns contained in theinput sentence (e.g., a sentence of the job description or the profile).

Embeddings may serve a key role in understanding properties containedwithin free-text fields. However, they ultimately have limitations.Although text can easily be converted to vectors of continuous values,vectors cannot be so easily converted to grammatically correct text.Also, understanding how text can be manipulated in order to move from astarting representation to a final representation while abiding toexplicit syntactical rules is unclear. Therefore, in some exampleembodiments, the optimization system 216 uses a quality profiledetection technique, in which suggested text, such as a suggested phraseof a recommendation, is provided as a tuple composed of a verb and anobject, in accordance with the subject-verb-object grammaticalstructure, with the subject implicitly being the author of a page (e.g.,the user to whom a resume or profile page at issue corresponds), theverb being a method of expression, and the object being the expression.

In some example embodiments, the optimization system 216 approaches theproblem of quality profile detection as two sub-problems—detection andranking. In the detection aspect, the optimization system 216 determineswhat a profile is expressing in its free-text fields. In the rankingaspect, the ranking aspect, the optimization system 216 determines whatcan be more voluminously expressed in a page of the user (e.g., in aprofile or resume of the user).

In some example embodiments, the optimization system 216 addresses thedetection aspect by, given a fixed vocabulary of verb and object types,V, and O, we can formulating the detection as a classification problem:arg max_(v∈V,o∈O) P(v,o|sentence).Regarding the previously-discussed embedding of arbitrary sentencelengths, the optimization system 216 may seed with pre-trainedembeddings for semantic similarity in formulating a classification todetect a fixed set of verb-object pairs:

${{argmax}_{{v \in V},{o \in O}}{\prod\limits_{s \in S}{P( {v, o \middle| {f(s)} } )}}},$where S represents all sentences that can be described by theVERB-OBJECT pair v-o.

Alternatively, since each sentence (or group of sentences) may bedescribed by multiple verb-object pairs, instead of optimizing outputfor the loss against a continuous value P, the optimization system 216may optimize against a binary vector of length |V|+|O|, where the first|V| dimensions can be mapped to a predefined dictionary of verbs, andthe remainder to a dictionary of predefined object types. This allowsthe optimization system 216 to represent a sentence or even a paragraphover a distribution of verbs and objects.

In some example embodiments, the optimization system 216 addressed theranking aspect using the goal of presenting users with actionablecomposition improvement to their pages, such as their resumes andprofile pages. In some example embodiments, the optimization system 216evaluates profiles based on generated recruiter interest, which may becaptured differently for two different job-seeking segments: activejobseekers and passive job seekers. For active jobseekers, success maybe measured after the user has applied for a job based on whether arecruiter e-mailed the user, such as to begin the interview process. Forpassive job seekers, success may be measured independently of the userapplying for a job based on a recruiter e-mail. Here, the optimizationsystem 216 may determine the job based on an aggregation of recent jobsthe recruiter may have posted. For example, if a user is contacted byfive recruiters, and responds to three of them, the job interests of theuser may be based on an aggregation of postings for those threerecruiters.

Using y=1 to represent success, the optimization system 216 may optimizefor:arg max_(v∈V,o∈O) P(y|g[ƒ(s)]+({right arrow over (v)},{right arrow over(o)}),{right arrow over (j)})−P(y|g[ƒ(s)],{right arrow over (j)}),where ƒ projects a sentence snippet s from a position description,summary, or title, into the semantic embedding space, g projects theembedded vector {right arrow over (s)} into the verb-object space,{right arrow over (v)} and {right arrow over (o)} are unit vectorsdefined over the verb and object vocabularies respectively, and {rightarrow over (j)} is a vector representation of a user's job interests.The embedding function ƒ can be used across multiple text snippets in asingle sentence classification. However, in this example, theoptimization system 216 independently projects two text snippets for asingle classification, a position title, and the block describing theposition. In some example embodiments, the P(y|g[ƒ(s)],{right arrow over(j)}) term may be dropped and the optimization system 216 optimizes for:arg max_(v∈V,o∈O) P(y|g[ƒ(s)]+({right arrow over (v)},{right arrow over(o)}),{right arrow over (j)}).

In some example embodiments, the end result is a given pairing (e.g.,title and work description), and the optimization system 216 ranks allV-O pairs that will most likely increase recruiter interest in aprofile. For example, if the top recommendation isQUANTIFY-ACHIEVEMENTS, this implies that adding measurables ofachievements to a work description will make a profile more interestingto a recruiter.

In some example embodiments, a component of profile optimization dependson an understanding of a user's job interest. To capture thisunderstanding, the optimization system 216 may utilize summarizationtechniques across the job postings a user has interacted with. In someexample embodiments, the optimization system 216 extracts candidatephrases from the text, such as based on part-of-speech sequences. Insome example embodiments, the optimization system 216 keeps only thosephrases that consist of zero or more adjectives followed by one ormultiple nouns. In some example embodiments, the optimization system 216also uses sentence embeddings to represent both the candidate phrasesand the document itself in the same high-dimensional vector space, andthen ranks the candidate phrases to select the output keyphrases. Inaddition, the optimization system 216 may improve the ranking step byproviding a way to tune the diversity of the extracted key phrases.

Although a brute-force method might consider all words and/or phrases ina document as candidate key phrases, such an approach has itsdisadvantages. Given computational costs of the brute-force method andthe fact that not all words and phrases in a document are equally likelyto convey its content, the optimization system 216 may employ heuristicsto identify a smaller subset of better candidates in performingcandidate phrase selection. Examples of heuristics that may be employedby the optimization system 216 include, but are not limited to, removingstop words and punctuation, filtering for words with certain parts ofspeech or, for multi-word phrases, certain part-of-speech (POS)patterns, and using external knowledge bases as a reference source ofgood/bad key phrases.

Rather than taking all of the n-grams (where 1≤n≤5), in some exampleembodiments, the optimization system 216 limits itself to only nounphrases matching the POS pattern {(<JJ>*<NN.*>+<IN>)?<JJ>*<NN.*>+},which matches any number of adjectives followed by at least one nounthat may be joined by a preposition to one other adjective(s)+noun(s)sequence. This POS pattern is just one example. The pattern may beexpanded to include other patterns as well.

In some example embodiments, the optimization system 216 generatesrecommendations for a single job for which there are sufficientindications that the user is or would be interested. The naive approachwould return the top N phrases most closely resembling the job postingfrom which they were extracted. In scenarios where users directly seethe extracted keyphrases (e.g., text summarization, tagging for search),this is problematic, as it may result in redundant keyphrases adverselyimpacting the user's experience, which can deteriorate to the point inwhich providing keyphrases becomes completely useless. Moreover, inextracting a fixed number of top keyphrases, redundancy hinders thediversification of the extracted keyphrases.

In some example embodiments, the optimization system 216 employs aMaximal Marginal Relevance (MMR) metric to solve the diversity problem.The use of the MMR metric combines in a controllable way the concepts ofrelevance and diversity. The following describes how to adapt MMR tokeyphrase extraction, in order to combine keyphrase informativeness withdissimilarity among selected keyphrases.

The original MMR from information retrieval and text summarization isbased on the set of all initially retrieved documents R for a giveninput query Q, and on an initially empty set S representing documentsthat are selected as good answers for Q. S is iteratively populated bycomputing MMR as described in the equation below, where D_(i) and D_(j)are retrieved documents, and Sim₁ and Sim₂ are similarity functions.

MMR = argmax_(D_(i) ∈ R ∖ S)[λ ⋅ Sim₁(D_(i), Q) − (1 − λ) ⋅ max_(D_(i) ∈ S)Sim₂(D_(i), D_(j))].

To use MMR to summarize a single job D_(i), the optimization system 216may adopt it to certain notation as follows:

MMR_(i) = argmax_(D_(ij) ∈ R ∖ S)  [λ ⋅ Sim₁(D_(i, j), D_(i)) − (1 − λ) ⋅ max_(D_(ik) ∈ S) Sim₂(D_(i, j), D_(i, k))],where R is the set of candidate keyphrases, S is the iterativelypopulated summary, D_(i) is the full document embedding, and D_(ij) andD_(ik) are the embeddings of candidate phrases j and k, respectively.

In some example embodiments, the optimization system 216 generatesrecommendations for multiple jobs for which there are sufficientindications that the user is or would be interested. The optimizationsystem 216 may extend the MMR technique for the multi-job-posting case,such as by using any of the following approaches.

In a first approach:

MMR = argmax_(D_(ij) ∈ R ∖ S)  [λ ⋅ Sim₁(D_(i, j), D_(i)) − (1 − λ) ⋅ max_(D_(ik) ∈ S)Sim₂(D_(i, j), D_(i, k))],where D is the document vector representing all jobs of interest to themember.

In a second approach:

MMR = argmax_(D_(ij) ∈ R ∖ S)  [λ ⋅ max_(D_(ik)∀k)Sim₁(D_(i, j), D_(i, k)) − (1 − λ) ⋅ max_(D_(ik) ∈ S)Sim₂(D_(i, j), D_(i, k))]where D is the document vector representing all jobs of interest to themember.

In a third approach:

${MMR} = {{{argmax}_{D_{ij} \in {R\backslash S}}\lbrack {{\lambda \cdot {{Sim}_{1}( {D_{i,j},D} )}} - {( {1 - \lambda} ) \cdot {\sum\limits_{D_{ik} \in S}{{Sim}_{2}( {D_{i,j},D_{i,k}} )}}}} \rbrack}.}$

In a fourth approach:

${MMR} = {{argmax}_{D_{ij} \in {R\backslash S}}{\quad{\lbrack {{\lambda \cdot {\max_{D_{ik}{\forall k}}{{Sim}_{1}( {D_{i,j},D_{i,k}} )}}} - {( {1 - \lambda} ) \cdot {\sum\limits_{D_{ik} \in S}{{Sim}_{2}( {D_{i,j},D_{i,k}} )}}}} \rbrack.}}}$

FIG. 16 is a block diagram illustrating a mobile device 1600, accordingto an example embodiment. The mobile device 1600 can include a processor1602. The processor 1602 can be any of a variety of different types ofcommercially available processors suitable for mobile devices 1600 (forexample, an XScale architecture microprocessor, a Microprocessor withoutInterlocked Pipeline Stages (MIPS) architecture processor, or anothertype of processor). A memory 1604, such as a random access memory (RAM),a Flash memory, or other type of memory, is typically accessible to theprocessor 1602. The memory 1604 can be adapted to store an operatingsystem (OS) 1606, as well as application programs 1608, such as a mobilelocation-enabled application that can provide location-based services(LBSs) to a user. The processor 1602 can be coupled, either directly orvia appropriate intermediary hardware, to a display 1610 and to one ormore input/output (I/O) devices 1612, such as a keypad, a touch panelsensor, a microphone, and the like. Similarly, in some embodiments, theprocessor 1602 can be coupled to a transceiver 1614 that interfaces withan antenna 1616. The transceiver 1614 can be configured to both transmitand receive cellular network signals, wireless data signals, or othertypes of signals via the antenna 1616, depending on the nature of themobile device 1600. Further, in some configurations, a GPS receiver 1618can also make use of the antenna 1616 to receive GPS signals.

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may constitute eithersoftware modules (e.g., code embodied (1) on a non-transitorymachine-readable medium or (2) in a transmission signal) orhardware-implemented modules. A hardware-implemented module is tangibleunit capable of performing certain operations and may be configured orarranged in a certain manner. In example embodiments, one or morecomputer systems (e.g., a standalone, client or server computer system)or one or more processors may be configured by software (e.g., anapplication or application portion) as a hardware-implemented modulethat operates to perform certain operations as described herein.

In various embodiments, a hardware-implemented module may be implementedmechanically or electronically. For example, a hardware-implementedmodule may comprise dedicated circuitry or logic that is permanentlyconfigured (e.g., as a special-purpose processor, such as a fieldprogrammable gate array (FPGA) or an application-specific integratedcircuit (ASIC)) to perform certain operations. A hardware-implementedmodule may also comprise programmable logic or circuitry (e.g., asencompassed within a general-purpose processor or other programmableprocessor) that is temporarily configured by software to perform certainoperations. It will be appreciated that the decision to implement ahardware-implemented module mechanically, in dedicated and permanentlyconfigured circuitry, or in temporarily configured circuitry (e.g.,configured by software) may be driven by cost and time considerations.

Accordingly, the term “hardware-implemented module” should be understoodto encompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired) or temporarily ortransitorily configured (e.g., programmed) to operate in a certainmanner and/or to perform certain operations described herein.Considering embodiments in which hardware-implemented modules aretemporarily configured (e.g., programmed), each of thehardware-implemented modules need not be configured or instantiated atany one instance in time. For example, where the hardware-implementedmodules comprise a general-purpose processor configured using software,the general-purpose processor may be configured as respective differenthardware-implemented modules at different times. Software mayaccordingly configure a processor, for example, to constitute aparticular hardware-implemented module at one instance of time and toconstitute a different hardware-implemented module at a differentinstance of time.

Hardware-implemented modules can provide information to, and receiveinformation from, other hardware-implemented modules. Accordingly, thedescribed hardware-implemented modules may be regarded as beingcommunicatively coupled. Where multiple of such hardware-implementedmodules exist contemporaneously, communications may be achieved throughsignal transmission (e.g., over appropriate circuits and buses) thatconnect the hardware-implemented modules. In embodiments in whichmultiple hardware-implemented modules are configured or instantiated atdifferent times, communications between such hardware-implementedmodules may be achieved, for example, through the storage and retrievalof information in memory structures to which the multiplehardware-implemented modules have access. For example, onehardware-implemented module may perform an operation, and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware-implemented module may then,at a later time, access the memory device to retrieve and process thestored output. Hardware-implemented modules may also initiatecommunications with input or output devices, and can operate on aresource (e.g., a collection of information).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods described herein may be at least partiallyprocessor-implemented. For example, at least some of the operations of amethod may be performed by one or more processors orprocessor-implemented modules. The performance of certain of theoperations may be distributed among the one or more processors, not onlyresiding within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor or processors maybe located in a single location (e.g., within a home environment, anoffice environment or as a server farm), while in other embodiments theprocessors may be distributed across a number of locations.

The one or more processors may also operate to support performance ofthe relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations may be performed by a group of computers (as examples ofmachines including processors), these operations being accessible via anetwork (e.g., the Internet) and via one or more appropriate interfaces(e.g., Application Program Interfaces (APIs)).

Example embodiments may be implemented in digital electronic circuitry,or in computer hardware, firmware, software, or in combinations of them.Example embodiments may be implemented using a computer program product,e.g., a computer program tangibly embodied in an information carrier,e.g., in a machine-readable medium for execution by, or to control theoperation of, data processing apparatus, e.g., a programmable processor,a computer, or multiple computers.

A computer program can be written in any form of programming language,including compiled or interpreted languages, and it can be deployed inany form, including as a stand-alone program or as a module, subroutine,or other unit suitable for use in a computing environment. A computerprogram can be deployed to be executed on one computer or on multiplecomputers at one site or distributed across multiple sites andinterconnected by a communication network.

In example embodiments, operations may be performed by one or moreprogrammable processors executing a computer program to performfunctions by operating on input data and generating output. Methodoperations can also be performed by, and apparatus of exampleembodiments may be implemented as, special purpose logic circuitry,e.g., a field programmable gate array (FPGA) or an application-specificintegrated circuit (ASIC).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. Inembodiments deploying a programmable computing system, it will beappreciated that both hardware and software architectures meritconsideration. Specifically, it will be appreciated that the choice ofwhether to implement certain functionality in permanently configuredhardware (e.g., an ASIC), in temporarily configured hardware (e.g., acombination of software and a programmable processor), or a combinationof permanently and temporarily configured hardware may be a designchoice. Below are set out hardware (e.g., machine) and softwarearchitectures that may be deployed, in various example embodiments.

FIG. 17 is a block diagram of an example computer system 1700 on whichmethodologies described herein may be executed, in accordance with anexample embodiment. In alternative embodiments, the machine operates asa standalone device or may be connected (e.g., networked) to othermachines. In a networked deployment, the machine may operate in thecapacity of a server or a client machine in server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine may be a personal computer (PC), atablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), acellular telephone, a web appliance, a network router, switch or bridge,or any machine capable of executing instructions (sequential orotherwise) that specify actions to be taken by that machine. Further,while only a single machine is illustrated, the term “machine” shallalso be taken to include any collection of machines that individually orjointly execute a set (or multiple sets) of instructions to perform anyone or more of the methodologies discussed herein.

The example computer system 1700 includes a processor 1702 (e.g., acentral processing unit (CPU), a graphics processing unit (GPU) orboth), a main memory 1704 and a static memory 1706, which communicatewith each other via a bus 1708. The computer system 1700 may furtherinclude a graphics display unit 1710 (e.g., a liquid crystal display(LCD) or a cathode ray tube (CRT)). The computer system 1700 alsoincludes an alphanumeric input device 1712 (e.g., a keyboard or atouch-sensitive display screen), a user interface (UI) navigation device1714 (e.g., a mouse), a storage unit 1716, a signal generation device1718 (e.g., a speaker) and a network interface device 1720.

The storage unit 1716 includes a machine-readable medium 1722 on whichis stored one or more sets of instructions and data structures (e.g.,software) 1724 embodying or utilized by any one or more of themethodologies or functions described herein. The instructions 1724 mayalso reside, completely or at least partially, within the main memory1704 and/or within the processor 1702 during execution thereof by thecomputer system 1700, the main memory 1704 and the processor 1702 alsoconstituting machine-readable media.

While the machine-readable medium 1722 is shown in an example embodimentto be a single medium, the term “machine-readable medium” may include asingle medium or multiple media (e.g., a centralized or distributeddatabase, and/or associated caches and servers) that store the one ormore instructions 1724 or data structures. The term “machine-readablemedium” shall also be taken to include any tangible medium that iscapable of storing, encoding or carrying instructions (e.g.,instructions 1724) for execution by the machine and that cause themachine to perform any one or more of the methodologies of the presentdisclosure, or that is capable of storing, encoding or carrying datastructures utilized by or associated with such instructions. The term“machine-readable medium” shall accordingly be taken to include, but notbe limited to, solid-state memories, and optical and magnetic media.Specific examples of machine-readable media include non-volatile memory,including by way of example semiconductor memory devices, e.g., ErasableProgrammable Read-Only Memory (EPROM), Electrically ErasableProgrammable Read-Only Memory (EEPROM), and flash memory devices;magnetic disks such as internal hard disks and removable disks;magneto-optical disks; and CD-ROM and DVD-ROM disks.

The instructions 1724 may further be transmitted or received over acommunications network 1726 using a transmission medium. Theinstructions 1724 may be transmitted using the network interface device1720 and any one of a number of well-known transfer protocols (e.g.,HTTP). Examples of communication networks include a local area network(“LAN”), a wide area network (“WAN”), the Internet, mobile telephonenetworks, Plain Old Telephone Service (POTS) networks, and wireless datanetworks (e.g., WiFi and WiMax networks). The term “transmission medium”shall be taken to include any intangible medium that is capable ofstoring, encoding or carrying instructions for execution by the machine,and includes digital or analog communications signals or otherintangible media to facilitate communication of such software.

The following numbered examples are embodiments.

-   -   1. A computer-implemented method comprising:        -   identifying, by a computer system having a memory and at            least one hardware processor, a plurality of job postings            published on an online service as corresponding to a type of            job based on corresponding feature data of each one of the            plurality of job postings;        -   extracting, by the computer system, a plurality of phrases            from the identified plurality of job postings based on a            corresponding relevancy measurement and a corresponding            diversity measurement for each one of the plurality of            phrases, the relevancy measurement comprising a measure of            relevance of the corresponding phrase to the type of job,            and the diversity measurement comprising a measure of            distinction between the corresponding phrase and other            phrases in the plurality of phrases;        -   for each one of the extracted plurality of phrases,            determining, by the computer system, a corresponding section            of a page of a first user to suggest for placement of the            extracted phrase using a placement classifier, the placement            classifier configured to determine the corresponding section            based on the extracted phrase;        -   for each one of the extracted plurality of phrases,            generating, by the computer system, a corresponding            recommendation for the page of the first user based on the            extracted phrase and the determined corresponding section of            the extracted phrase, the corresponding recommendation            comprising a suggested addition of the corresponding            extracted phrase to the corresponding section of the page of            the first user; and        -   causing, by the computer system, the generated            recommendations to be displayed on a first computing device            of the first user.    -   2. The computer-implemented method of example 1, wherein the        causing the generated recommendations to be displayed on the        first computing device of the first user comprises causing a        corresponding selectable user interface element to be displayed        in association with each one of the generated recommendations,        and the computer-implemented method further comprises:        -   receiving, by the computer system, a user selection of the            corresponding selectable user interface element of one of            the displayed recommendations from the first computing            device of the first user;        -   in response to the user selection, causing, by the computer            system, the extracted phrase corresponding to the selected            user interface element to be displayed in a text field of            the determined corresponding section of the extracted phrase            on the first computing device of the first user, the text            field being configured to receive user-entered text;        -   receiving, by the computer system, an instruction from the            first computing device of the first user to save the            user-entered text that is in the text field to the            determined corresponding section of the page of the first            user, the user-entered text comprising at least a portion of            the extracted phrase corresponding to the selected user            interface element; and        -   storing, by the computer system, the user-entered text            including the at least a portion of the extracted phrase in            a database in association with the determined corresponding            section of the page of the first user.    -   3. The computer-implemented method of example 2, further        comprising:        -   receiving, by the computer system, a request to view the            page of the first user from a second computing device of a            second user; and        -   causing, by the computer system, the page of the first user            to be displayed on the second computing device of the second            user, the page comprising the user-entered text including            the at least a portion of the extracted phrase.    -   4. The computer-implemented method of any one of examples 1 to        3, wherein the page comprises a profile page of the first user        that is associated with a profile of the first user, the profile        being stored in a database of the online service in association        with a profile of the first user.    -   5. The computer-implemented method of any one of examples 1 to        4, wherein the page comprises a resume of the first user that is        included in an application to a job posting via the online        service.    -   6. The computer-implemented method of any one of examples 1 to        5, wherein the corresponding feature data of each one of the        plurality of job postings comprises at least one of a role        within an organization, a seniority level, an industry, and a        job function.    -   7. The computer-implemented method of any one of examples 1 to        6, wherein, for each one of the extracted plurality of phrases,        the corresponding section of the page comprises one of a summary        section of a profile, a work experience section of the profile,        an education section of the profile, a skills section of the        profile, and an accomplishments section of the profile.    -   8. The computer-implemented method of any one of examples 1 to        7, further comprising:        -   accessing, by the computer system, a profile of the first            user stored in a database;        -   generating, by the computer system, a suggestion for adding            a measurable accomplishment to a particular section of the            profile of the first user based on profile data of the            accessed profile using a neural network model, the neural            network model being configured to identify the measurable            accomplishment within the profile data of the accessed            profile; and        -   causing, by the computer system, the generated suggestion            for adding the measurable accomplishment to be displayed on            the first computing device of the first user.    -   9. A computer-implemented method comprising:        -   receiving, by a computer system having a memory and at least            one hardware processor, a plurality of job postings            published on an online service;        -   determining, by the computer system, that a subset of the            plurality of the job postings satisfies a similarity            criteria based on corresponding feature data of each job            posting in the subset, the subset comprising multiple job            postings;        -   selecting, by the computer system, the subset of the            plurality of job postings based on the determining that the            subset satisfies the similarity criteria;        -   generating, by the computer system, a recommendation for a            page of a first user based on the selected subset of job            postings, the recommendation comprising a suggested addition            of content to the page of the first user; and        -   causing, by the computer system, the generated            recommendation for the page of the first user to be            displayed on a computing device of the first user.    -   10. The computer-implemented method of example 9, wherein the        receiving the plurality of job postings comprises:        -   accessing user activity data of the first user stored in a            database in association with a profile of the first user;        -   determining that the user activity data indicates an            interest by the first user in the plurality of job postings;            and        -   selecting the plurality of job postings based on the            determining that the user activity data indicates an            interest by the first user in the plurality of job openings.    -   11. The computer-implemented method of example 10, wherein the        user activity data comprises at least one of viewing a job        listing and submitting an application for a job listing.    -   12. The computer-implemented method of any one of examples 9 to        11, wherein the determining that the subset of the plurality of        the job postings satisfies the similarity criteria comprises        using at least one filter to determine that the corresponding        feature data of each job posting in the subset of the plurality        of job postings matches a filter feature data.    -   13. The computer-implemented method of any one of examples 9 to        12, wherein the determining that the subset of the plurality of        the job postings satisfies the similarity criteria comprises        using semantic matching to determine that the corresponding        feature data of each job posting in the subset of the plurality        of job postings comprises a similar meaning as the corresponding        feature data of the other job postings in the subset of the        plurality of job postings.    -   14. The computer-implemented method of any one of examples 9 to        13, wherein the corresponding feature data of each one of the        subset of the plurality of job postings comprises at least one        of a role within an organization, a seniority level, an        industry, and a job function.    -   15. The computer-implemented method of any one of examples 9 to        14, wherein the page comprises a profile page of the first user        that is associated with a profile of the first user, the profile        being stored in a database of an online service in association        with a profile of the first user.    -   16. The computer-implemented method of any one of examples 9 to        15, wherein the page comprises a resume of the first user that        is included in an application to a job posting via an online        service.    -   17. A computer-implemented method comprising:        -   receiving, by a computer system having a memory and at least            one hardware processor, a plurality of phrases for a type of            job;        -   selecting, by the computer system, a group of phrases from            the plurality of phrases based on a corresponding relevancy            measurement and a corresponding diversity measurement for            each phrase in the selected group of phrases, the relevancy            measurement comprising a measure of relevance of the            corresponding selected phrase in the selected group of            phrases to the type of job, and the diversity measurement            comprising a measure of distinction between each phrase in            the selected group of phrases and other phrases in the            selected group of the phrases;        -   generating, by the computer system, a recommendation for a            page of a first user based on the selected group of phrases,            the recommendation comprising a suggested addition of the            selected group of phrases to the page of the first user; and        -   causing, by the computer system, the generated            recommendation for the page of the first user to be            displayed on a computing device of the first user.    -   18. The computer-implemented method of example 17, wherein the        selecting the group of phrases from the plurality of phrases        comprises:        -   for each one of the plurality of phrases, generating the            corresponding relevance measurement;        -   ranking the plurality of phrases based on their            corresponding relevance measurements;        -   selecting a first phrase of the plurality of phrases for            inclusion in the group of phrases based on the first phrase            having a highest ranking amongst the plurality of phrases;        -   identifying a second phrase of the plurality of phrases            based on the second phrase having a second highest ranking            amongst the plurality of phrases;        -   determining a diversity measurement of the second phrase            indicating the measure of distinction between the second            phrase and the first phrase; and        -   determining whether or not to include the second phrase in            the group of phrases based on the determined diversity            measurement of the second phrase.    -   19. The computer-implemented method of example 18, wherein the        determining whether or not to include the second phrase in the        group of phrases comprises including the second phrase in the        group of phrases based on the determined diversity measurement        of the second phrase.    -   20. The computer-implemented method of example 18, wherein the        determining whether or not to include the second phrase in the        group of phrases comprises excluding the second phrase from the        group of phrases based on the determined diversity measurement        of the second phrase.    -   21. The computer-implemented method of any one of examples 17 to        20, wherein the receiving the plurality of phrases for the type        of job comprises:        -   selecting sentences from one or more job listings of the            type of job based on the selected sentences being determined            to comprise role-dependent information that corresponds to a            role in an organization; and        -   extracting noun phrases from the selected sentences, the            extracted noun phrases being included in the plurality of            phrases, and a remaining portion of the selected sentences            other than the extracted noun phrases being omitted from the            plurality of phrases.    -   22. The computer-implemented method of any one of examples 17 to        21, wherein the receiving the plurality of phrases for the type        of job comprises extracting the plurality of phrases from one or        more job listings of the type of job.    -   23. The computer-implemented method of any one of examples 17 to        22, wherein the page comprises a profile page of the first user        that is associated with a profile of the first user, the profile        being stored in a database of an online service in association        with a profile of the first user.    -   24. The computer-implemented method of any one of examples 17 to        23, wherein the page comprises a resume of the first user that        is included in an application to a job posting of the type of        job via an online service.    -   25. A computer-implemented method comprising:        -   receiving, by a computer system having a memory and at least            one hardware processor, a plurality of phrases;        -   for each one of the plurality of phrases, selecting, by the            computer system, a corresponding section of a page of a            first user to suggest for placement of the phrase from            amongst a plurality of sections using a placement            classifier, the placement classifier configured to determine            the corresponding section based on the phrase;        -   for each one of the plurality of phrases, generating, by the            computer system, a corresponding recommendation for the page            of a first user based on the phrase and the determined            corresponding section of the page of the first user, the            recommendation comprising a suggested addition of the phrase            to the determined corresponding section of the page of the            first user; and        -   causing, by the computer system, the generated            recommendations for the page of the first user to be            displayed on a first computing device of the first user.    -   26. The computer-implemented method of example 25, wherein the        plurality of sections comprises at least one of a summary        section, a skill section, a work experience section, and an        education section.    -   27. The computer-implemented method of example 25 or example 26,        wherein the causing the generated recommendations to be        displayed on the first computing device of the first user        comprises causing a corresponding selectable user interface        element to be displayed in association with each one of the        generated recommendations, and the computer-implemented method        further comprises:        -   receiving, by the computer system, a user selection of the            corresponding selectable user interface element of one of            the displayed recommendations from the first computing            device of the first user; and        -   in response to the user selection, generating, by the            computer system, causing the extracted phrase corresponding            to the selected user interface element to be displayed in a            text field of the determined corresponding section of the            extracted phrase on the first computing device of the first            user, the text field being configured to receive            user-entered text.    -   28. The computer-implemented method of example 27, further        comprising:        -   receiving, by the computer system, an instruction from the            first computing device of the first user to save the            user-entered text that is in the text field to the            determined corresponding section of the page of the first            user, the user-entered text comprising at least a portion of            the extracted phrase corresponding to the selected user            interface element; and        -   storing, by the computer system, the user-entered text            including the at least a portion of the extracted phrase in            a database in association with the determined corresponding            section of the page of the first user.    -   29. The computer-implemented method of example 28, further        comprising using the received instruction to save the        user-entered text to the determined corresponding section of the        page of the first user as training data in a machine learning        algorithm configured to train the placement classifier.    -   30. The computer-implemented method of example 27, further        comprising:        -   receiving, by the computer system, an instruction from the            first computing device of the first user to save the            user-entered text that is in the text field to a different            section o of the page of the first user other than the            determined corresponding section, the user-entered text            comprising at least a portion of the extracted phrase            corresponding to the selected user interface element; and        -   storing, by the computer system, the user-entered text            including the at least a portion of the extracted phrase in            a database in association with the different section of the            page of the first user.    -   31. The computer-implemented method of example 30, further        comprising using the received instruction to save the        user-entered text to the different section of the page of the        first user as training data in a machine learning algorithm        configured to train the placement classifier.    -   32. The computer-implemented method of any one of examples 25 to        31, wherein the page comprises a profile page of the first user        that is associated with a profile of the first user, the profile        being stored in a database of an online service in association        with a profile of the first user.    -   33. The computer-implemented method of any one of examples 25 to        32, wherein the page comprises a resume of the first user that        is included in an application to a job posting of the type of        job via an online service.    -   34. A computer-implemented method comprising:        -   accessing, by a computer system having a memory and at least            one hardware processor, a profile of a first user of an            online service stored in a database of the online service;        -   generating, by the computer system, a suggestion for adding            a measurable accomplishment to a particular section of a            page of the first user based on profile data of the accessed            profile using a neural network model, the neural network            model being configured to identify the measurable            accomplishment based on the profile data of the accessed            profile; and        -   causing, by the computer system, the generated suggestion            for adding the measurable accomplishment to be displayed on            a first computing device of the first user.    -   35. The computer-implemented method of example 34, wherein the        profile data comprises a current job title of the first user and        textual data distinct from the current job title, and the neural        network model is configured to identify the measurable        accomplishment based on the current job title of the first user        and the textual data.    -   36. The computer-implemented method of example 35, wherein the        textual data comprises text from a summary section of the        profile of the first user or text from a work experience section        of the profile of the first user, and the measurable        accomplishment comprises at least a portion of the textual data.    -   37. The computer-implemented method of example 36, wherein the        profile data further comprises at least one of a seniority level        of the first user, a location of the first user, an industry of        the first user, and a role of the first user within an        organization.    -   38. The computer-implemented method of any one of examples 34 to        37, wherein the causing the generated suggestion to be displayed        comprises causing a selectable user interface element to be        displayed in association with the generated suggestion, and the        computer-implemented method further comprises:        -   receiving, by the computer system, a user selection of the            selectable user interface element of one of the displayed            suggestion from the first computing device of the first            user;        -   in response to the user selection, causing, by the computer            system, the measurable accomplishment to be displayed in a            text field of the particular section of the page of the            first user on the first computing device of the first user,            the text field being configured to receive user-entered            text;        -   receiving, by the computer system, an instruction from the            first computing device of the first user to save the            user-entered text that is in the text field to the            particular section of the page of the first user, the            user-entered text comprising at least a portion of the            measurable accomplishment; and        -   storing, by the computer system, the user-entered text            including the at least a portion of the measurable            accomplishment in a database in association with the            particular section of the page of the first user.    -   39. The computer-implemented method of any one of examples 34 to        38, wherein the particular section of the page comprises a        summary section of the page or a work experience section of the        page.    -   40. The computer-implemented method of any one of examples 34 to        39, wherein the page comprises a profile page of the first user        that is associated with the profile of the first user.    -   41. The computer-implemented method of any one of examples 34 to        40, wherein the page comprises a resume of the first user that        is included in an application to a job posting of a type of job        via the online service.    -   42. A computer-implemented method comprising:        -   training, by a computer system having a memory and at least            one hardware processor, a classifier using a first plurality            of training data, each one of the first plurality of            training data comprising profile data of a user, textual            data distinct from the profile data, and a label indicating            whether or not the one of the first plurality of training            data qualifies as a measurable accomplishment;        -   for each one of a first plurality of sample data,            generating, by the computer system, a corresponding            likelihood value indicating a likelihood that the one of the            first plurality of sample data corresponds to a measurable            accomplishment using the trained classifier, each one of the            first plurality of sample data comprising profile data of a            user and textual data distinct from the profile data;        -   identifying, by the computer system, a portion of the first            plurality of sample data as corresponding to confused            predictions based on the corresponding likelihood values of            the portion of the first plurality of sample data and a            confusion criteria; and        -   retraining, by the computer system, the trained classifier            using a second plurality of training data, the second            plurality of training data including the portion of the            first plurality of sample data based on the identifying of            the portion of the first plurality of sample data as            corresponding to confused prediction, each one of the second            plurality of training data comprising profile data of a            user, textual data distinct from the profile data, and a            label indicating whether or not the one of the second            plurality of training data qualifies as a measurable            accomplishment.    -   43. The computer-implemented method of claim 42, wherein the        confusion criteria comprises the corresponding likelihood value        being below a minimum threshold value or above a maximum        threshold value.    -   44. The computer-implemented method of claim 42, wherein the        confusion criteria comprises:        -   a difference between the corresponding likelihood value of            one of the portion of the plurality of sample data and the            corresponding likelihood value of another one of the portion            of the plurality of sample data is greater than a threshold            difference value; and        -   a difference between the textual data of the one of the            portion of the plurality of sample data and the textual data            of the other one of the portion of the plurality of sample            data is less than a threshold textual difference.    -   45. The computer-implemented method of claim 42, further        comprising:        -   accessing, by the computer system, a profile of a first user            of an online service stored in a database of the online            service;        -   identifying, by the computer system, a measurable            accomplishment of the first user based on profile data of            the accessed profile of the first user using the retrained            classifier;        -   generating, by the computer system, a suggestion for adding            the identified measurable accomplishment to a particular            section of a page of the first user; and        -   causing, by the computer system, the generated suggestion            for adding the measurable accomplishment to be displayed on            a first computing device of the first user    -   46. The computer-implemented method of claim 45, wherein the        profile data comprises a current job title of the first user and        textual data distinct from the current job title, and the neural        network model is configured to identify the measurable        accomplishment based on the current job title of the first user        and the textual data.    -   47. The computer-implemented method of claim 46, wherein the        textual data comprises text from a summary section of the        profile of the first user or text from a work experience section        of the profile of the first user, and the measurable        accomplishment comprises at least a portion of the textual data.    -   48. The computer-implemented method of claim 47, wherein the        profile data further comprises at least one of a seniority level        of the first user, a location of the first user, an industry of        the first user, and a role of the first user within an        organization.    -   49. The computer-implemented method of claim 45, wherein the        causing the generated suggestion to be displayed comprises        causing a selectable user interface element to be displayed in        association with the generated suggestion, and the        computer-implemented method further comprises:        -   receiving, by the computer system, a user selection of the            selectable user interface element of one of the displayed            suggestion from the first computing device of the first            user;        -   in response to the user selection, causing, by the computer            system, the measurable accomplishment to be displayed in a            text field of the particular section of the page of the            first user on the first computing device of the first user,            the text field being configured to receive user-entered            text;        -   receiving, by the computer system, an instruction from the            first computing device of the first user to save the            user-entered text that is in the text field to the            particular section of the page of the first user, the            user-entered text comprising at least a portion of the            measurable accomplishment; and        -   storing, by the computer system, the user-entered text            including the at least a portion of the measurable            accomplishment in a database in association with the            particular section of the page of the first user.    -   50. The computer-implemented method of claim 45, wherein the        particular section of the page comprises a summary section of        the page or a work experience section of the page.    -   51. The computer-implemented method of claim 45, wherein the        page comprises a profile page of the first user that is        associated with the profile of the first user.    -   52. The computer-implemented method of claim 45, wherein the        page comprises a resume of the first user that is included in an        application to a job posting of a type of job via the online        service.    -   53. A system comprising:        -   at least one processor; and        -   a non-transitory computer-readable medium storing executable            instructions that, when executed, cause the at least one            processor to perform the method of any one of examples 1 to            52.    -   54. A non-transitory machine-readable storage medium, tangibly        embodying a set of instructions that, when executed by at least        one processor, causes the at least one processor to perform the        method of any one of examples 1 to 52.    -   55. A machine-readable medium carrying a set of instructions        that, when executed by at least one processor, causes the at        least one processor to carry out the method of any one of        examples 1 to 52.

Although an embodiment has been described with reference to specificexample embodiments, it will be evident that various modifications andchanges may be made to these embodiments without departing from thebroader spirit and scope of the present disclosure. Accordingly, thespecification and drawings are to be regarded in an illustrative ratherthan a restrictive sense. The accompanying drawings that form a parthereof, show by way of illustration, and not of limitation, specificembodiments in which the subject matter may be practiced. Theembodiments illustrated are described in sufficient detail to enablethose skilled in the art to practice the teachings disclosed herein.Other embodiments may be utilized and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. This Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.Although specific embodiments have been illustrated and describedherein, it should be appreciated that any arrangement calculated toachieve the same purpose may be substituted for the specific embodimentsshown. This disclosure is intended to cover any and all adaptations orvariations of various embodiments. Combinations of the aboveembodiments, and other embodiments not specifically described herein,will be apparent to those of skill in the art upon reviewing the abovedescription.

What is claimed is:
 1. A computer-implemented method comprising:receiving, by a computer system having a memory and at least onehardware processor, a plurality of phrases; for each one of theplurality of phrases, selecting, by the computer system, a correspondingsection of a page of a first user to suggest for placement of the phrasefrom amongst a plurality of sections using a placement classifier, theplacement classifier configured to determine the corresponding sectionbased on the phrase; for each one of the plurality of phrases,generating, by the computer system, a corresponding recommendation forthe page of a first user based on the phrase and the determinedcorresponding section of the page of the first user, the recommendationcomprising a suggested addition of the phrase to the determinedcorresponding section of the page of the first user; and causing, by thecomputer system, the generated recommendations for the page of the firstuser to be displayed on a first computing device of the first user. 2.The computer-implemented method of claim 1, wherein the plurality ofsections comprises at least one of a summary section, a skill section, awork experience section, and an education section.
 3. Thecomputer-implemented method of claim 1, wherein the causing thegenerated recommendations to be displayed on the first computing deviceof the first user comprises causing a corresponding selectable userinterface element to be displayed in association with each one of thegenerated recommendations, and the computer-implemented method furthercomprises: receiving, by the computer system, a user selection of thecorresponding selectable user interface element of one of the displayedrecommendations from the first computing device of the first user; andin response to the user selection, generating, by the computer system,causing the extracted phrase corresponding to the selected userinterface element to be displayed in a text field of the determinedcorresponding section of the extracted phrase on the first computingdevice of the first user, the text field being configured to receiveuser-entered text.
 4. The computer-implemented method of claim 3,further comprising: receiving, by the computer system, an instructionfrom the first computing device of the first user to save theuser-entered text that is in the text field to the determinedcorresponding section of the page of the first user, the user-enteredtext comprising at least a portion of the extracted phrase correspondingto the selected user interface element; and storing, by the computersystem, the user-entered text including the at least a portion of theextracted phrase in a database in association with the determinedcorresponding section of the page of the first user.
 5. Thecomputer-implemented method of claim 4, further comprising using thereceived instruction to save the user-entered text to the determinedcorresponding section of the page of the first user as training data ina machine learning algorithm configured to train the placementclassifier.
 6. The computer-implemented method of claim 3, furthercomprising: receiving, by the computer system, an instruction from thefirst computing device of the first user to save the user-entered textthat is in the text field to a different section of the page of thefirst user other than the determined corresponding section, theuser-entered text comprising at least a portion of the extracted phrasecorresponding to the selected user interface element; and storing, bythe computer system, the user-entered text including the at least aportion of the extracted phrase in a database in association with thedifferent section of the page of the first user.
 7. Thecomputer-implemented method of claim 6, further comprising using thereceived instruction to save the user-entered text to the differentsection of the page of the first user as training data in a machinelearning algorithm configured to train the placement classifier.
 8. Thecomputer-implemented method of claim 1, wherein the page comprises aprofile page of the first user that is associated with a profile of thefirst user, the profile being stored in a database of an online servicein association with a profile of the first user.
 9. Thecomputer-implemented method of claim 1, wherein the page comprises aresume of the first user that is included in an application to a jobposting of the type of job via an online service.
 10. A systemcomprising: at least one hardware processor; and a non-transitorymachine-readable medium embodying a set of instructions that, whenexecuted by the at least one hardware processor, cause the at least onehardware processor to perform operations, the operations comprising:receiving a plurality of phrases; for each one of the plurality ofphrases, selecting a corresponding section of a page of a first user tosuggest for placement of the phrase from amongst a plurality of sectionsusing a placement classifier, the placement classifier configured todetermine the corresponding section based on the phrase; for each one ofthe plurality of phrases, generating a corresponding recommendation forthe page of a first user based on the phrase and the determinedcorresponding section of the page of the first user, the recommendationcomprising a suggested addition of the phrase to the determinedcorresponding section of the page of the first user; and causing thegenerated recommendations for the page of the first user to be displayedon a first computing device of the first user.
 11. The system of claim10, wherein the plurality of sections comprises at least one of asummary section, a skill section, a work experience section, and aneducation section.
 12. The system of claim 10, wherein the causing thegenerated recommendations to be displayed on the first computing deviceof the first user comprises causing a corresponding selectable userinterface element to be displayed in association with each one of thegenerated recommendations, and the operations further comprise:receiving a user selection of the corresponding selectable userinterface element of one of the displayed recommendations from the firstcomputing device of the first user; and in response to the userselection, generating causing the extracted phrase corresponding to theselected user interface element to be displayed in a text field of thedetermined corresponding section of the extracted phrase on the firstcomputing device of the first user, the text field being configured toreceive user-entered text.
 13. The system of claim 12, wherein theoperations further comprise: receiving an instruction from the firstcomputing device of the first user to save the user-entered text that isin the text field to the determined corresponding section of the page ofthe first user, the user-entered text comprising at least a portion ofthe extracted phrase corresponding to the selected user interfaceelement; and storing the user-entered text including the at least aportion of the extracted phrase in a database in association with thedetermined corresponding section of the page of the first user.
 14. Thesystem of claim 13, wherein the operations further comprise using thereceived instruction to save the user-entered text to the determinedcorresponding section of the page of the first user as training data ina machine learning algorithm configured to train the placementclassifier.
 15. The system of claim 12, wherein the operations furthercomprise: receiving an instruction from the first computing device ofthe first user to save the user-entered text that is in the text fieldto a different section of the page of the first user other than thedetermined corresponding section, the user-entered text comprising atleast a portion of the extracted phrase corresponding to the selecteduser interface element; and storing the user-entered text including theat least a portion of the extracted phrase in a database in associationwith the different section of the page of the first user.
 16. Thecomputer-implemented method of claim 15, wherein the operations furthercomprise using the received instruction to save the user-entered text tothe different section of the page of the first user as training data ina machine learning algorithm configured to train the placementclassifier.
 17. The system of claim 10, wherein the page comprises aprofile page of the first user that is associated with a profile of thefirst user, the profile being stored in a database of an online servicein association with a profile of the first user.
 18. The system of claim10, wherein the page comprises a resume of the first user that isincluded in an application to a job posting of the type of job via anonline service.
 19. A non-transitory machine-readable medium embodying aset of instructions that, when executed by at least one hardwareprocessor, cause the at least one hardware processor to performoperations, the operations comprising: receiving a plurality of phrases;for each one of the plurality of phrases, selecting a correspondingsection of a page of a first user to suggest for placement of the phrasefrom amongst a plurality of sections using a placement classifier, theplacement classifier configured to determine the corresponding sectionbased on the phrase; for each one of the plurality of phrases,generating a corresponding recommendation for the page of a first userbased on the phrase and the determined corresponding section of the pageof the first user, the recommendation comprising a suggested addition ofthe phrase to the determined corresponding section of the page of thefirst user; and causing the generated recommendations for the page ofthe first user to be displayed on a first computing device of the firstuser.
 20. The non-transitory machine-readable medium of claim 19,wherein the plurality of sections comprises at least one of a summarysection, a skill section, a work experience section, and an educationsection.